System

Abstract

To provide a system which enables efficient communication between a supporter and a person to be supported and enables even an ordinary person having no expert knowledge to perform support activity.SOLUTION: The system includes a means for a user to input a specific question requiring support, a means for analyzing the question using a generative AI and generating an optimal answer, a means for providing the generated answer to the user and collecting feedback from the user, a means for learning the generative AI based on the collected feedback, a means for identifying a supporter and notifying the supporter of a support request, a means for giving points based on the answer provided by the supporter, and a means for managing the points and allowing the supporter to exchange the points for goods or services.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, each generation needs support in various areas. However, with conventional support methods, it is often difficult to ensure efficient communication between the supporter and the recipient, making it difficult for ordinary people without specialized knowledge to provide support. Furthermore, despite the labor-intensive nature of support activities, compensation is often not properly assessed. Furthermore, as a large amount of data accumulates, there is a lack of means to improve the accuracy of systems based on appropriate feedback. These issues need to be resolved. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input a specific question for which support is required, a means for analyzing the question using a generation AI and generating an optimal answer, a means for providing the generated answer to the user and collecting feedback from the user, a means for training the generation AI based on the collected feedback, a means for identifying supporters and notifying the supporters of support requests, a means for awarding points based on the answers provided by the supporters, and a means for managing the points and allowing the supporters to exchange the points for goods or services. This enables efficient communication between supporters and support recipients, allowing even ordinary people without specialized knowledge to provide support. Furthermore, compensation for support activities is appropriately assessed, allowing support activities to be carried out as a side job. Furthermore, the generation AI learns based on accumulated data, improving the accuracy of the entire support system.

[0006] "User" refers to a person who accesses the system to input questions or receive assistance.

[0007] "Generative AI" refers to artificial intelligence technology that analyzes questions entered by users and generates optimal answers.

[0008] "Questions" refer to questions that users have about the system or requests for assistance.

[0009] "Answer" refers to the solution or information provided by the generating AI or a helper in response to a question.

[0010] "Feedback" refers to a user's feedback or comments on a provided answer.

[0011] "Supporter" refers to a person who provides answers to questions through the system.

[0012] "Support request" refers to a notification requesting a supporter to create an answer to a user's question.

[0013] "Points" refer to units of reward awarded to supporters when they provide answers.

[0014] "Goods or Services" means any tangible item or benefit that may be redeemed using Points.

[0015] "Learning" refers to the process by which generative AI improves its performance based on collected feedback. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention provides an effective support system for both the supporter and the support recipient by allowing the user to input a specific question for which support is needed and having a generation AI provide the optimal answer. An embodiment of this system is described in detail below.

[0038] 1. User registration and initial settings

[0039] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0040] 2. Accepting support requests

[0041] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[0042] 3. Providing and Evaluating Answers

[0043] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. For example, the answer might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the provided answer and enters feedback. The device sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0044] 4. Matching with supporters

[0045] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0046] 5. Points allocation and management

[0047] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0048] 6. Data accumulation and analysis

[0049] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0050] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improve the accuracy of support activities, and enable support activities to be pursued as a career or side job through a points system. In this way, we provide a system that improves the overall quality and effectiveness of support and contributes to society as a whole.

[0051] The processing flow will be explained below.

[0052] Program processing steps

[0053] 1. User registration and initial settings

[0054] Step 1:

[0055] The user installs the application on the device and launches the application.

[0056] Step 2:

[0057] The user enters their name, email address, and password on the account creation screen.

[0058] The terminal transmits the input information to the server.

[0059] Step 3:

[0060] The server stores the user information in a database and generates a user ID.

[0061] Step 4:

[0062] The server returns the generated user ID to the terminal.

[0063] The terminal saves the user ID and moves to the next screen.

[0064] Step 5:

[0065] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0066] The terminal transmits the selected category information to the server.

[0067] Step 6:

[0068] The server stores the category information in a database.

[0069] 2. Accepting support requests

[0070] Step 7:

[0071] The user enters the specific question for which they need assistance.

[0072] The terminal sends the entered question and user ID to the server.

[0073] Step 8:

[0074] The server analyzes the content of the question using natural language processing technology.

[0075] The server extracts appropriate categories and keywords and passes them to the generation AI.

[0076] 3. Generate and provide answers

[0077] Step 9:

[0078] The generative AI generates the optimal answer based on the question it receives.

[0079] The generation AI sends the generated answer back to the server.

[0080] Step 10:

[0081] The server sends the answer received from the generation AI to the user.

[0082] The terminal receives the response and displays it to the user.

[0083] 4. User Feedback

[0084] Step 11:

[0085] The user may enter their satisfaction with the answers provided and any additional feedback.

[0086] The terminal transmits the feedback information to the server.

[0087] Step 12:

[0088] The server stores the received feedback in a database and uses it as training data for the generative AI.

[0089] 5. Matching with supporters (if necessary)

[0090] Step 13:

[0091] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[0092] Step 14:

[0093] The server notifies the supporter of the support request.

[0094] Step 15:

[0095] The supporter receives the request, reviews the details, and creates a response.

[0096] Step 16:

[0097] The supporter's terminal transmits the created answer to the server.

[0098] Step 17:

[0099] The server provides the answers from the supporters to the user.

[0100] 6. Points allocation and management

[0101] Step 18:

[0102] If the supporter's answer is accepted, the server calculates points based on the point rules.

[0103] The server assigns points to the supporter's account.

[0104] Step 19:

[0105] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[0106] The supporter's terminal transmits this information to the server.

[0107] Step 20:

[0108] The server accepts the point exchange request and carries out the exchange procedure.

[0109] summary

[0110] This series of steps allows for efficient support between users and supporters, and optimal answers are provided using generative AI. Supporters can also receive appropriate rewards through a points system, improving the accuracy and effectiveness of the overall support system.

[0111] Example 1

[0112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] To address today's diverse support needs, systems are needed that allow users to easily input specific questions and quickly provide optimal answers. However, existing support systems have the following problems. First, they lack a process for clarifying the category of support a user needs, making it difficult to provide accurate support. Furthermore, there is no established method for collecting and utilizing feedback to improve the quality of answers generated by generative AI, resulting in low accuracy of answers. Finally, there is a lack of appropriate incentives for supporters, making it difficult for them to continue participating.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0115] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and for the supporter to exchange the points for goods or services; means for installing an application on a terminal and transmitting a name, email address, and password to the server; means for the user to select a category for which assistance is required and transmit that information to the server; means for the server to accumulate user information and feedback in a database; and means for the generation AI to train to improve its performance based on the accumulated data. This enables the system to accurately and efficiently provide the assistance required by the user, improve the accuracy of the generation AI's answers, and provide appropriate incentives to supporters, thereby achieving continuous and effective assistance.

[0116] "User" refers to an individual or group of people who require assistance and utilize the system to input a specific question.

[0117] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate optimal answers.

[0118] A "server" refers to a computer system that receives and stores information from users, interacts with the generating AI, and notifies supporters of requests.

[0119] "Supporter" refers to an individual or group who uses their specialized knowledge to provide answers to users' questions and earn points.

[0120] A "database" refers to a storage device for storing and managing user information, support activity data, feedback, etc.

[0121] "Points" refer to the rewards given to supporters for the answers they provide, and are units within the system that can be accumulated and exchanged for goods and services.

[0122] "Feedback" refers to ratings and comments that users make on answers provided.

[0123] "Installation" refers to the process of placing an application on a device and making it available for use.

[0124] A "question" refers to a sentence or group of sentences that a user inputs to the system with the specific matter for which they require assistance.

[0125] A "category" refers to a group for classifying the area of ​​support that a user needs.

[0126] A "prompt" refers to a question or instruction used to give appropriate instructions to the generative AI.

[0127] MODE FOR CARRYING OUT THE INVENTION

[0128] The present invention is a support system in which a user inputs a specific question, and a generation AI analyzes the question and provides the most appropriate answer. This system is composed of a user, a terminal, a server, and a generation AI. Detailed embodiments are described below.

[0129] User registration and initial settings

[0130] First, the user installs the application on their device. Once the installation is complete, the user launches the application and enters their name, email address, and password on the account creation screen. The device sends this information to the server. The server stores the received information in a database and generates a unique user ID. The generated user ID is sent back to the device. Next, the user selects the category for which they need assistance and sends this information to the server. The server stores the selected information in a database.

[0131] Accepting support requests

[0132] The user inputs a specific question for which they require assistance. For example, they can input a question such as, "What supermarkets in Tokyo are recommended?" The device then sends this question and the user ID to the server. The server stores the received question in a database and analyzes it using natural language processing (NLP) technology. Specifically, it uses libraries such as spaCy to extract appropriate categories and keywords from the question. The server then passes the analysis results to a generative AI, which generates a prompt. An example of a prompt is, "Generate an appropriate answer to the following question: 'What supermarkets in Tokyo are recommended?'"

[0133] Providing and Evaluating Answers

[0134] The generative AI analyzes the question based on the prompt text and generates an appropriate answer. The generated answer is sent back to the server and stored in a database. The server then sends the answer to the device and provides it to the user. The user checks the answer and enters feedback. For example, the answer provided might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the answer and sends feedback information to the server via their device. The server stores the feedback in a database and uses this data to learn and improve the performance of the generative AI.

[0135] Matching with supporters

[0136] If the question requires specialized knowledge, the server identifies an appropriate assistant from the list of assistants. The server notifies the assistant of the request for assistance, and the assistant receives the request. The assistant creates a specific answer and sends it to the server via the terminal. The server stores the answer in a database and provides it to the user.

[0137] Points allocation and management

[0138] The server awards points to supporters based on feedback from users. Points are calculated according to the quality of answers and the user's satisfaction. Supporters can check their points and request exchange for goods or services via their terminals. The server accepts the request and carries out the necessary procedures.

[0139] Data accumulation and analysis

[0140] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this data to learn and improve its performance. This enables it to provide more accurate answers in future support activities.

[0141] This system allows users to receive accurate and prompt assistance, and the helpers can earn points as an incentive. The generative AI can improve its performance through feedback, thereby increasing the overall quality of assistance.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user installs the application on their device. After installation, they launch the application and enter their name, email address, and password on the account creation screen. The device sends this input data to the server. The server stores the received user information in a database, generates a unique user ID, and sends it back to the device. Specifically, the server creates a new record in the database, stores the name, email address, and password, and simultaneously generates an incrementing user ID.

[0145] Step 2:

[0146] The user selects the category for which they need assistance (e.g., general life, exams, employment). The device sends the selected category information along with the user ID to the server. The server saves this information in a database. Specifically, the server associates the selected category information with the user ID generated earlier in the database and stores it.

[0147] Step 3:

[0148] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device sends the question and the user ID to the server. The server then stores the received question in a database. Specifically, the server creates a new question record in the database and stores the question text and the user ID.

[0149] Step 4:

[0150] The server analyzes the received question using natural language processing (NLP) technology. For example, it uses spaCy to tokenize the text and extract keywords. The input is the question text, and the output is a list of extracted keywords. For example, the server analyzes the question "What supermarkets are recommended in Tokyo?" and extracts keywords such as "Tokyo," "recommended," and "supermarket."

[0151] Step 5:

[0152] The server generates a prompt based on the analysis results (extracted keywords and question text). The generated prompt is then sent to the generation AI (e.g., GPT-3). An example of a prompt is: "Please generate an appropriate answer to the following question: 'Please tell me the best supermarkets in Tokyo.'" The input is the analysis results (keyword list and question text), and the output is the generated prompt. Specifically, the server uses a prompt generation algorithm to construct a prompt and send it to the generation AI.

[0153] Step 6:

[0154] The generation AI generates an appropriate answer based on the prompt text. The generation AI receives the prompt text from the server and generates the answer text using an internal model. The input is the prompt text and the output is the answer text. Specifically, the generation AI goes through a generation process to generate an answer such as "A popular supermarket in Tokyo is XX supermarket" and sends this back to the server.

[0155] Step 7:

[0156] The server receives the generated answer and stores it in a database. It then sends the answer to the terminal and provides it to the user. The input is the answer text from the generation AI, and the output is the answer provided to the user. Specifically, the server creates a new answer record in the database, associates it with the user ID, and stores the answer text.

[0157] Step 8:

[0158] The user receives the response and checks its contents. They then enter feedback such as their level of satisfaction and areas for improvement. The device then sends the feedback information and the user ID to the server. The server then stores this feedback information in a database. The input is the user's feedback, and the output is the stored feedback data. Specifically, the server adds a feedback record to the database and associates it with the user ID.

[0159] Step 9:

[0160] The server uses the collected feedback as learning data to improve the performance of the generative AI. It analyzes the feedback information and adds it to the generative AI's training dataset. The input is user feedback, and the output is an updated training dataset. Specifically, the server analyzes the feedback data and provides new learning data to the generative AI.

[0161] Step 10:

[0162] If the question requires specialized knowledge, the server identifies an appropriate supporter from the supporter list and notifies the supporter of the support request. The input is the question content and analysis results, and the output is a request notification to the supporter. Specifically, the server searches the supporter list and sends a notification to the most suitable supporter.

[0163] Step 11:

[0164] The supporter receives the request and creates a specific answer. The answer is sent to the server via the terminal. The server stores the supporter's answer in a database and provides it to the user. The input is the answer created by the supporter, and the output is the answer provided to the user. In concrete terms, the server stores the received answer in a database and provides it to the user.

[0165] Step 12:

[0166] The server awards points to supporters based on user feedback. Points are calculated based on the quality of the answers and the user's satisfaction. The input is the feedback information, and the output is the awarded points. Specifically, the server calculates points using a feedback evaluation algorithm and adds them to the supporter's account.

[0167] Step 13:

[0168] Supporters use their terminals to check their points and request exchange for goods or services. The server accepts the request and carries out the necessary procedures. The input is the supporter's point exchange request, and the output is the provision of goods or services. Specifically, the server processes the exchange request and carries out the procedures to provide the supporter with goods or services.

[0169] (Application example 1)

[0170] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0171] Conventional food delivery systems require users to manually search for restaurants and menus, making it difficult to find the right option in the process. They also lack a mechanism for providing appropriate answers to user questions, resulting in a poor user experience. Furthermore, there is no mechanism for actively using user feedback to train the generative AI and improve the accuracy of answers, making it difficult to improve the quality of the service. There is a need for a system that solves these problems, allows users to easily enjoy optimal food delivery services, and improves the overall quality of the service.

[0172] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0173] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; and means for a user to input a specific question about food delivery and for the generation AI to suggest optimal stores and menus. This allows a user to receive optimal suggestions from the generation AI simply by inputting a specific question, and also enables the quality of service to be continuously improved through feedback.

[0174] "Generative AI" is an artificial intelligence technology that analyzes specific questions entered by users and generates optimal answers based on them.

[0175] A "support request" is request information sent to a supporter to ask for an answer to a user's question.

[0176] "Points" are units of virtual currency or credit awarded as a reward to supporters when they provide specific answers.

[0177] "Feedback" refers to information that a user returns through an application, such as their satisfaction with the answers provided, their ratings, or their opinions.

[0178] "Food delivery" is a system in which you order food and drinks and have them delivered to a designated location via a delivery service.

[0179] "User registration" is the process in which a user enters basic information such as name, email address, and password in order to use an application, and the server manages this information in a database.

[0180] A "supporter" is a person or system that has the ability to provide specialized knowledge and information in response to a user's questions.

[0181] The "server" is a central control device that receives input information and feedback from users and provides information to the generation AI and assistants.

[0182] A "database" is an information system that allows the server to centrally manage various data such as user information, questions, answers, feedback, and points.

[0183] "Means for suggesting optimal restaurants and menus" refers to a method that uses generative AI to recommend appropriate restaurants and dishes in response to specific food delivery-related questions from users.

[0184] The present invention is a system that improves the user experience in food delivery by using generative AI to provide optimal answers to specific questions entered by users. An embodiment of this system is described in detail below.

[0185] 1. User registration and initial settings

[0186] First, the user installs the application on their smartphone, smart glasses, or head-mounted display. When launching the application for the first time, the user enters basic information such as name, email address, and password on the account creation screen, which is then sent from the device to the server. The server then stores the user information in a database and generates a user ID. The user then selects a support category related to food delivery, and the device sends this information to the server. The server then stores the selected information in a database.

[0187] 2. Accepting support requests

[0188] The user inputs a specific question related to food delivery, such as "What do you recommend for dinner tonight?" The device then sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[0189] 3. Providing and Evaluating Answers

[0190] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user. For example, this answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here." The user checks the provided answer and enters feedback on the content. The device sends the feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0191] 4. Matching with supporters

[0192] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[0193] 5. Points allocation and management

[0194] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[0195] 6. Data accumulation and analysis

[0196] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0197] Hardware and Software Used

[0198] Hardware:

[0199] Smartphones (general smart devices)

[0200] Smart glasses (general-purpose smart wearable device)

[0201] Head-mounted display (general VR / AR device)

[0202] software:

[0203] Generative AI models (natural language processing models such as GPT-3)

[0204] Server-side database (MySQL, PostgreSQL)

[0205] Specific examples

[0206] User Ask: "What are your dinner recommendations for tonight?"

[0207] Generative AI response: "The nearby restaurant, Restaurant A, has a good reputation. You can make a reservation here."

[0208] In this way, the system of the present invention provides quick and appropriate answers to user questions about food delivery, improving the user experience and utilizing feedback to continuously improve the accuracy of the service.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The user installs the application on their smartphone, smart glasses, or head-mounted display and launches it for the first time. The user enters basic information such as name, email address, and password on the account creation screen. The entered information is sent by the device to the server. The server stores the user information in a database and generates a user ID. The user selects a support category related to food delivery, and this information is also sent to the server and stored in the database.

[0212] Input: Name, Email Address, Password, Support Category

[0213] Output: User ID, user information stored in the database

[0214] Step 2:

[0215] Users input specific questions about food delivery into the app. For example, "What do you recommend for dinner tonight?" The input question and user ID are sent to the server by the device. The server analyzes the received question and extracts appropriate categories and keywords. The analysis results are then passed to the generation AI, which generates the optimal answer.

[0216] Input: Question, User ID

[0217] Output: Analysis results of the question, data passed to the generation AI

[0218] Step 3:

[0219] The generation AI understands the user's question and generates an appropriate answer. The generated answer is sent back to the server, which then provides the answer to the user. For example, the answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here."

[0220] Input: User question, analysis result of the generating AI

[0221] Output: Generated answer, information provided to the user

[0222] Step 4:

[0223] The user checks the answers provided and enters feedback on the content into the app. The feedback information is sent by the device to the server, which stores it in a database. The collected feedback is used as learning data for the generative AI.

[0224] Input: Feedback information

[0225] Output: Feedback stored in a database, training data for generative AI

[0226] Step 5:

[0227] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[0228] Input: Supporter list, support request

[0229] Output: Supporter's response, providing the response to the user

[0230] Step 6:

[0231] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[0232] Input: Supporter's answer, point rules

[0233] Output: Points awarded to supporters, product exchange procedure

[0234] Step 7:

[0235] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0236] Input: Outreach data

[0237] Output: Improved performance of generative AI, more accurate answers

[0238] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0239] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[0240] 1. User registration and initial settings

[0241] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0242] 2. Accepting support requests

[0243] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[0244] 3. Emotion Recognition and Answer Generation

[0245] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[0246] 4. Providing and Evaluating Answers

[0247] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0248] 5. Matching with supporters

[0249] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0250] 6. Points allocation and management

[0251] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0252] 7. Data accumulation and analysis

[0253] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0254] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improving the accuracy of support activities, and by combining it with an emotion engine, provides answers that take the user's emotions into consideration. In addition, a points system allows supporters to receive appropriate rewards, improving the accuracy and effectiveness of the entire support system.

[0255] The processing flow will be explained below.

[0256] Program processing steps

[0257] 1. User registration and initial settings

[0258] Step 1:

[0259] The user installs the application on the device and launches the application.

[0260] Step 2:

[0261] The user enters their name, email address, and password on the account creation screen.

[0262] The terminal transmits the input information to the server.

[0263] Step 3:

[0264] The server stores the user information in a database and generates a user ID.

[0265] Step 4:

[0266] The server returns the generated user ID to the terminal.

[0267] The terminal saves the user ID and moves to the next screen.

[0268] Step 5:

[0269] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0270] The terminal transmits the selected category information to the server.

[0271] Step 6:

[0272] The server stores the category information in a database.

[0273] 2. Accepting support requests

[0274] Step 7:

[0275] The user enters the specific question for which they need assistance.

[0276] The terminal sends the entered question and user ID to the server.

[0277] Step 8:

[0278] The server analyzes the content of the question using natural language processing technology.

[0279] Step 9:

[0280] The server extracts appropriate categories and keywords and passes them to the generation AI.

[0281] 3. Emotion Recognition and Answer Generation

[0282] Step 10:

[0283] The server sends the question content to the emotion engine.

[0284] The emotion engine recognizes the user's emotion based on the user's input.

[0285] Step 11:

[0286] The emotion engine returns the recognized emotion data to the server.

[0287] The server sends the emotion data to the generation AI.

[0288] Step 12:

[0289] The generative AI generates the optimal answer based on the question content and emotional data.

[0290] Step 13:

[0291] The generation AI sends the generated answer back to the server.

[0292] 4. Providing and Evaluating Answers

[0293] Step 14:

[0294] The server sends the answer received from the generation AI to the user.

[0295] Step 15:

[0296] The terminal receives the response and displays it to the user.

[0297] Step 16:

[0298] The user may enter their satisfaction with the answers provided and any additional feedback.

[0299] Step 17:

[0300] The terminal transmits the feedback information to the server.

[0301] Step 18:

[0302] The server stores the received feedback in a database and uses it as training data for the generative AI.

[0303] 5. Matching with supporters

[0304] Step 19:

[0305] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[0306] Step 20:

[0307] The server notifies the supporter of the support request.

[0308] Step 21:

[0309] The supporter receives the request, reviews the details, and creates a response.

[0310] Step 22:

[0311] The supporter's terminal transmits the created answer to the server.

[0312] Step 23:

[0313] The server provides the answers from the supporters to the user.

[0314] 6. Points allocation and management

[0315] Step 24:

[0316] If the supporter's answer is accepted, the server calculates points based on the point rules.

[0317] Step 25:

[0318] The server assigns points to the supporter's account.

[0319] Step 26:

[0320] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[0321] Step 27:

[0322] The supporter's terminal transmits this information to the server.

[0323] Step 28:

[0324] The server accepts the point exchange request and carries out the exchange procedure.

[0325] 7. Data accumulation and analysis

[0326] Step 29:

[0327] The server stores all support activity data (questions, answers, feedback, emotional data, etc.) in a database.

[0328] Step 30:

[0329] The generative AI uses this accumulated data to learn and improve its own performance.

[0330] summary

[0331] This series of steps allows for efficient support between users and supporters, and the generative AI provides optimal answers. Furthermore, by combining it with an emotion engine, answers can be provided that take the user's emotions into consideration. Furthermore, supporters can receive appropriate rewards through a point system, improving the accuracy and effectiveness of the overall support system.

[0332] Example 2

[0333] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] Conventional support systems have had the problem that the answers users receive to their questions are not always appropriate to the user's feelings or situation. Furthermore, the answers provided do not fully utilize the supporter's expertise, limiting the quality and effectiveness of the support. Furthermore, supporters are not properly compensated, which can lead to a decline in motivation for support activities.

[0335] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0336] In this invention, the server includes: a means for inputting a specific question for which a user requires assistance; a means for analyzing the question using a generation AI and generating an optimal answer; a means for providing the generated answer to the user and collecting feedback from the user; a means for training the generation AI based on the collected feedback; a means for recognizing the user's emotions based on the content of the question; a means for transmitting emotion data to the generation AI and generating an answer that takes emotions into consideration; a means for identifying a supporter and notifying the supporter of a support request; a means for awarding points based on the answer provided by the supporter; and a means for managing the points and allowing the supporter to exchange the points for goods or services. This enables the provision of answers that take the user's emotions into consideration and high-quality support by matching the user with supporters with specialized knowledge. Furthermore, the point system can provide appropriate rewards to supporters, thereby increasing their motivation for support activities.

[0337] "User" refers to a person who uses the system to seek assistance.

[0338] A "specific question requiring assistance" refers to a question that includes a specific problem or uncertainty that the user wants resolved.

[0339] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate the most appropriate answers.

[0340] An "emotion engine" refers to a system that recognizes a user's emotions based on the questions they enter and provides this as data to the generative AI.

[0341] "Feedback" refers to ratings and comments that users make on answers provided.

[0342] A "supporter" refers to a person who has specific knowledge and skills and whose role is to provide expert answers to users' questions.

[0343] "Points" refer to a form of reward given to a supporter when the supporter provides appropriate support to a user.

[0344] "Generated answer" refers to an answer generated by the generation AI based on the user's question.

[0345] "Notifying" refers to the act of informing relevant parties of specific information.

[0346] "Exchange" refers to the act of converting awarded points into concrete value such as goods or services.

[0347] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[0348] User registration and initial settings

[0349] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0350] Accepting support requests

[0351] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to a generative AI, which generates the optimal answer. The generative AI model used here is an advanced natural language processing model such as GPT-4.

[0352] Emotion Recognition and Answer Generation

[0353] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[0354] Providing and Evaluating Answers

[0355] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0356] Matching with supporters

[0357] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0358] Points allocation and management

[0359] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0360] Data accumulation and analysis

[0361] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0362] Specific examples

[0363] User: "What supermarkets in Tokyo do you recommend?"

[0364] Generative AI response: "Don't worry, Ameyoko Market is popular."

[0365] Prompt Sentence Examples

[0366] A user has asked, "What supermarkets are recommended in Tokyo?" You can sense the user's impatience. Please generate an answer that introduces appropriate supermarkets in a way that is considerate of the user.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1: User registration and initial setup

[0369] Input: The user enters their name, email address, and password.

[0370] How it works: After installing the app, the user launches it for the first time and is taken to the account creation screen. The user enters their name, email address, and password.

[0371] Data processing: The terminal sends this information to the server, which stores the user information in a database and generates a user ID.

[0372] Output: The server returns the generated user ID to the terminal, and the user ID is displayed on the terminal.

[0373] Step 2: Select a support category

[0374] Input: The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0375] How it works: The user selects an assistance category in the settings screen within the app.

[0376] Data processing: The terminal sends the selected information to the server, which stores it in a database.

[0377] Output: The server returns a save completion message to the terminal, and the support category selected by the user is displayed on the terminal.

[0378] Step 3: Accepting a request for assistance

[0379] Input: The user enters a specific question. Example: "What supermarkets in Tokyo do you recommend?"

[0380] Operation: The user enters a question on the question screen and presses the send button.

[0381] Data processing: The device sends the question and user ID to the server. The server then analyzes the received question using natural language processing technology and extracts appropriate categories and keywords.

[0382] Output: The server passes the analysis results to the generation AI to obtain the optimal answer. The generated answer is saved on the server.

[0383] Step 4: Emotion recognition and answer generation

[0384] Input: The emotion engine receives the question. At the same time, the analysis results are sent to the generation AI.

[0385] How it works: The emotion engine analyzes the question and recognizes the user's emotions. For example, it analyzes the question "I need help urgently" and determines that the user is in a hurry.

[0386] Data processing: Emotional data is sent to the generation AI, which then generates an answer based on the emotional data and analysis results.

[0387] Output: The generative AI generates an answer that takes emotions into account, and this answer is sent back to the server.

[0388] Step 5: Provide answers and gather feedback

[0389] Input: Generated Answer

[0390] Operation: The server generates a response and sends it to the user's device. The user receives the response and checks its contents.

[0391] Data processing: User feedback is input and the device sends it to the server.

[0392] Output: The feedback stored on the server is recorded in a database and used as training data for the generative AI.

[0393] Step 6: Matching with donors

[0394] Input: Question content and user category information

[0395] How it works: The server reviews the question and determines if expertise is required. It then identifies an appropriate helper from the helper list and notifies them of the assistance request.

[0396] Data processing: After the supporter receives the request, they create a specific response and send it to the server.

[0397] Output: The server receives the supporter's answer and provides it to the user.

[0398] Step 7: Earn and manage your points

[0399] Input: Accepted supporter's answer, point rules

[0400] Action: The server awards points to the supporter.

[0401] Data processing: Points will be added to the supporter's account.

[0402] Output: Supporters can check their points and request to exchange them for goods or services through their terminal. The server accepts the exchange request and carries out the goods exchange procedure.

[0403] Step 8: Data collection and analysis

[0404] Input: All support activity data, including questions, answers, feedback, and emotional data

[0405] How it works: All data is stored on the server.

[0406] Data processing: The generative AI learns based on this accumulated data and aims to improve its performance.

[0407] Output: A more accurate answer is generated.

[0408] (Application example 2)

[0409] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0410] Conventional support systems have been inadequate in responding to users' emotional states and specific needs, leaving the improvement of user experience a challenge. Furthermore, in the content distribution service field, it has been difficult to accurately recommend content that users want to watch. This has made it difficult to provide the support users require quickly and appropriately.

[0411] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; means for recognizing the user's emotions using an emotion engine and generating an optimal answer taking those emotions into consideration; and means for recommending content that the user wants to view in a content distribution service. This makes it possible to provide appropriate assistance that takes the user's emotions and needs into consideration and accurately recommend content that the user wants to view.

[0412] A "user" is an individual who uses the support system and inputs a specific question for which an answer is sought.

[0413] A "supporter" is an individual or organization that has specialized knowledge and experience and provides answers to users' questions.

[0414] "Generative AI" is an artificial intelligence technology that analyzes input questions and generates optimal answers.

[0415] An "emotion engine" is a technology that recognizes emotions from user input and generates appropriate answers based on that.

[0416] "Feedback" refers to ratings and additional comments from users who receive answers.

[0417] "Points" are units within the system that are awarded as part of the reward to supporters and can be exchanged for goods and services.

[0418] A "content distribution service" is an online service that provides digital content such as movies, music, and books.

[0419] A "viewing history" is a record of content that a user has viewed in the past.

[0420] "Recommendation means" is a technology that selects and provides optimal content based on the user's questions, emotional state, viewing history, etc.

[0421] The "server" is a central system that manages data, analyzes questions, runs generative AI, operates the emotion engine, collects feedback, and manages points.

[0422] This invention is a system that allows users to input specific questions for which they need assistance and provides optimal answers by combining generative AI and an emotion engine. In particular, it includes a function for recommending content based on the user's viewing history and emotions in content distribution services.

[0423] 1. User registration and initial settings

[0424] First, the user installs the application on their smartphone and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which is then sent to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they require (e.g., movie recommendations, music recommendations), and the device sends this information to the server. The server saves the user's selection in a database.

[0425] 2. Accepting support requests

[0426] The user inputs a specific question for which they require assistance. For example, "I'm looking for a relaxing movie." The device sends the question and the user ID to the server. The server analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[0427] 3. Emotion Recognition and Answer Generation

[0428] The emotion engine recognizes emotions based on the user's input. For example, if a user sends a request such as "I feel like watching a funny movie lately," the emotion engine recognizes that the user is looking for relaxation and fun. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more in line with the user's needs. For example, a response such as "What do you think of the latest popular comedy movies?"

[0429] 4. Providing and Evaluating Answers

[0430] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0431] 5. Matching with supporters

[0432] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0433] 6. Points allocation and management

[0434] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0435] 7. Data accumulation and analysis

[0436] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0437] Specific examples

[0438] For example, if a user asks, "I'm tired these days, so I'm looking for a relaxing movie," the emotion engine recognizes the user's desire to relax. Based on that, the generative AI generates the answer, "How about a relaxing movie that's popular these days to help you relax?"

[0439] Prompt Sentence Examples

[0440] "I'm looking for a relaxing movie."

[0441] "I feel like watching a funny movie lately."

[0442] As described above, the embodiments of the invention are configured to understand the feelings and needs of the user and provide appropriate content and support.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] A user installs the application on their smartphone and launches it for the first time. The user enters their name, email address, and password on the account creation screen, and the device sends this to the server. The server receives the user information as input data, generates a user ID, and stores this information in a database. The server generates a user ID as output data and sends it to the device.

[0446] Step 2:

[0447] The user enters a specific question for which they require assistance into the application. For example, they might enter a question like, "I'm looking for a relaxing movie." The device then sends the user ID and the question to the server. The server receives the user's question and ID as input data, analyzes the question using natural language processing technology, and extracts appropriate categories and keywords. The analyzed categories and keywords are then generated as output data and passed to the next processing step.

[0448] Step 3:

[0449] The server passes the analysis results to the generation AI, which generates the optimal answer. The generation AI receives the analyzed categories and keywords as input data and generates the optimal answer based on this. As a specific data calculation, it generates related answer candidates and selects the optimal one from among them. The generated answer is obtained as output data.

[0450] Step 4:

[0451] The emotion engine recognizes emotions based on the user's input. Specifically, it analyzes the text content of the user's input and uses an algorithm to determine the emotional state. It receives the user's question text as input data and generates the recognized emotional state as output data. The server sends the emotion data recognized by the emotion engine to the generation AI.

[0452] Step 5:

[0453] Generative AI takes emotional data into consideration to generate answers that are more in line with the user's needs. For example, it generates an answer such as, "How about watching a relaxing movie that's popular these days to soothe your fatigue?" It receives the emotion recognition results and question analysis results as input data, and adjusts the answer based on the emotion as data processing. It generates the final answer as output data.

[0454] Step 6:

[0455] The server provides the generated answer to the user. The server sends the answer received from the generation AI to the user's device, where it is received by the user. The server receives the generated answer as input data and sends it to the user's device as output data.

[0456] Step 7:

[0457] The user inputs their satisfaction with the answers provided and any additional feedback. The device then sends the feedback information to the server. The server receives the feedback as input data and stores it in a database. The feedback information is used as data for training the generative AI.

[0458] Step 8:

[0459] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server notifies the supporter of the support request and provides input data for the supporter to create a specific answer. The server then provides the supporter's answer to the user, delivering appropriate support to the user as output data.

[0460] Step 9:

[0461] Once the response is accepted, the server will award points to the supporter based on the point rules. The supporter will check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept this request and execute the goods exchange procedure. It will receive the supporter's point award information and exchange request as input data, and output confirmation of the goods exchange as output data.

[0462] Step 10:

[0463] The server stores all support activity data (question content, answers, feedback, emotional data, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity. The server receives the stored support activity data as input data and generates learning data as output data that contributes to improving the performance of the generating AI.

[0464] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0465] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0466] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0467] [Second embodiment]

[0468] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0469] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0470] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0471] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0472] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0473] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0474] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0475] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0476] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0477] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0478] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0479] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0480] The present invention provides an effective support system for both the supporter and the support recipient by allowing the user to input a specific question for which support is needed and having a generation AI provide the optimal answer. An embodiment of this system is described in detail below.

[0481] 1. User registration and initial settings

[0482] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0483] 2. Accepting support requests

[0484] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[0485] 3. Providing and Evaluating Answers

[0486] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. For example, the answer might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the provided answer and enters feedback. The device sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0487] 4. Matching with supporters

[0488] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0489] 5. Points allocation and management

[0490] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0491] 6. Data accumulation and analysis

[0492] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0493] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improve the accuracy of support activities, and enable support activities to be pursued as a career or side job through a points system. In this way, we provide a system that improves the overall quality and effectiveness of support and contributes to society as a whole.

[0494] The processing flow will be explained below.

[0495] Program processing steps

[0496] 1. User registration and initial settings

[0497] Step 1:

[0498] The user installs the application on the device and launches the application.

[0499] Step 2:

[0500] The user enters their name, email address, and password on the account creation screen.

[0501] The terminal transmits the input information to the server.

[0502] Step 3:

[0503] The server stores the user information in a database and generates a user ID.

[0504] Step 4:

[0505] The server returns the generated user ID to the terminal.

[0506] The terminal saves the user ID and moves to the next screen.

[0507] Step 5:

[0508] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0509] The terminal transmits the selected category information to the server.

[0510] Step 6:

[0511] The server stores the category information in a database.

[0512] 2. Accepting support requests

[0513] Step 7:

[0514] The user enters the specific question for which they need assistance.

[0515] The terminal sends the entered question and user ID to the server.

[0516] Step 8:

[0517] The server analyzes the content of the question using natural language processing technology.

[0518] The server extracts appropriate categories and keywords and passes them to the generation AI.

[0519] 3. Generate and provide answers

[0520] Step 9:

[0521] The generative AI generates the optimal answer based on the question it receives.

[0522] The generation AI sends the generated answer back to the server.

[0523] Step 10:

[0524] The server sends the answer received from the generation AI to the user.

[0525] The terminal receives the response and displays it to the user.

[0526] 4. User Feedback

[0527] Step 11:

[0528] The user may enter their satisfaction with the answers provided and any additional feedback.

[0529] The terminal transmits the feedback information to the server.

[0530] Step 12:

[0531] The server stores the received feedback in a database and uses it as training data for the generative AI.

[0532] 5. Matching with supporters (if necessary)

[0533] Step 13:

[0534] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[0535] Step 14:

[0536] The server notifies the supporter of the support request.

[0537] Step 15:

[0538] The supporter receives the request, reviews the details, and creates a response.

[0539] Step 16:

[0540] The supporter's terminal transmits the created answer to the server.

[0541] Step 17:

[0542] The server provides the answers from the supporters to the user.

[0543] 6. Points allocation and management

[0544] Step 18:

[0545] If the supporter's answer is accepted, the server calculates points based on the point rules.

[0546] The server assigns points to the supporter's account.

[0547] Step 19:

[0548] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[0549] The supporter's terminal transmits this information to the server.

[0550] Step 20:

[0551] The server accepts the point exchange request and carries out the exchange procedure.

[0552] summary

[0553] This series of steps allows for efficient support between users and supporters, and optimal answers are provided using generative AI. Supporters can also receive appropriate rewards through a points system, improving the accuracy and effectiveness of the overall support system.

[0554] Example 1

[0555] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0556] To address today's diverse support needs, systems are needed that allow users to easily input specific questions and quickly provide optimal answers. However, existing support systems have the following problems. First, they lack a process for clarifying the category of support a user needs, making it difficult to provide accurate support. Furthermore, there is no established method for collecting and utilizing feedback to improve the quality of answers generated by generative AI, resulting in low accuracy of answers. Finally, there is a lack of appropriate incentives for supporters, making it difficult for them to continue participating.

[0557] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0558] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and for the supporter to exchange the points for goods or services; means for installing an application on a terminal and transmitting a name, email address, and password to the server; means for the user to select a category for which assistance is required and transmit that information to the server; means for the server to accumulate user information and feedback in a database; and means for the generation AI to train to improve its performance based on the accumulated data. This enables the system to accurately and efficiently provide the assistance required by the user, improve the accuracy of the generation AI's answers, and provide appropriate incentives to supporters, thereby achieving continuous and effective assistance.

[0559] "User" refers to an individual or group of people who require assistance and utilize the system to input a specific question.

[0560] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate optimal answers.

[0561] A "server" refers to a computer system that receives and stores information from users, interacts with the generating AI, and notifies supporters of requests.

[0562] "Supporter" refers to an individual or group who uses their specialized knowledge to provide answers to users' questions and earn points.

[0563] A "database" refers to a storage device for storing and managing user information, support activity data, feedback, etc.

[0564] "Points" refer to the rewards given to supporters for the answers they provide, and are units within the system that can be accumulated and exchanged for goods and services.

[0565] "Feedback" refers to ratings and comments that users make on answers provided.

[0566] "Installation" refers to the process of placing an application on a device and making it available for use.

[0567] A "question" refers to a sentence or group of sentences that a user inputs to the system with the specific matter for which they require assistance.

[0568] A "category" refers to a group for classifying the area of ​​support that a user needs.

[0569] A "prompt" refers to a question or instruction used to give appropriate instructions to the generative AI.

[0570] MODE FOR CARRYING OUT THE INVENTION

[0571] The present invention is a support system in which a user inputs a specific question, and a generation AI analyzes the question and provides the most appropriate answer. This system is composed of a user, a terminal, a server, and a generation AI. Detailed embodiments are described below.

[0572] User registration and initial settings

[0573] First, the user installs the application on their device. Once the installation is complete, the user launches the application and enters their name, email address, and password on the account creation screen. The device sends this information to the server. The server stores the received information in a database and generates a unique user ID. The generated user ID is sent back to the device. Next, the user selects the category for which they need assistance and sends this information to the server. The server stores the selected information in a database.

[0574] Accepting support requests

[0575] The user inputs a specific question for which they require assistance. For example, they can input a question such as, "What supermarkets in Tokyo are recommended?" The device then sends this question and the user ID to the server. The server stores the received question in a database and analyzes it using natural language processing (NLP) technology. Specifically, it uses libraries such as spaCy to extract appropriate categories and keywords from the question. The server then passes the analysis results to a generative AI, which generates a prompt. An example of a prompt is, "Generate an appropriate answer to the following question: 'What supermarkets in Tokyo are recommended?'"

[0576] Providing and Evaluating Answers

[0577] The generative AI analyzes the question based on the prompt text and generates an appropriate answer. The generated answer is sent back to the server and stored in a database. The server then sends the answer to the device and provides it to the user. The user checks the answer and enters feedback. For example, the answer provided might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the answer and sends feedback information to the server via their device. The server stores the feedback in a database and uses this data to learn and improve the performance of the generative AI.

[0578] Matching with supporters

[0579] If the question requires specialized knowledge, the server identifies an appropriate assistant from the list of assistants. The server notifies the assistant of the request for assistance, and the assistant receives the request. The assistant creates a specific answer and sends it to the server via the terminal. The server stores the answer in a database and provides it to the user.

[0580] Points allocation and management

[0581] The server awards points to supporters based on feedback from users. Points are calculated according to the quality of answers and the user's satisfaction. Supporters can check their points and request exchange for goods or services via their terminals. The server accepts the request and carries out the necessary procedures.

[0582] Data accumulation and analysis

[0583] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this data to learn and improve its performance. This enables it to provide more accurate answers in future support activities.

[0584] This system allows users to receive accurate and prompt assistance, and the helpers can earn points as an incentive. The generative AI can improve its performance through feedback, thereby increasing the overall quality of assistance.

[0585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] The user installs the application on their device. After installation, they launch the application and enter their name, email address, and password on the account creation screen. The device sends this input data to the server. The server stores the received user information in a database, generates a unique user ID, and sends it back to the device. Specifically, the server creates a new record in the database, stores the name, email address, and password, and simultaneously generates an incrementing user ID.

[0588] Step 2:

[0589] The user selects the category for which they need assistance (e.g., general life, exams, employment). The device sends the selected category information along with the user ID to the server. The server saves this information in a database. Specifically, the server associates the selected category information with the user ID generated earlier in the database and stores it.

[0590] Step 3:

[0591] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device sends the question and the user ID to the server. The server then stores the received question in a database. Specifically, the server creates a new question record in the database and stores the question text and the user ID.

[0592] Step 4:

[0593] The server analyzes the received question using natural language processing (NLP) technology. For example, it uses spaCy to tokenize the text and extract keywords. The input is the question text, and the output is a list of extracted keywords. For example, the server analyzes the question "What supermarkets are recommended in Tokyo?" and extracts keywords such as "Tokyo," "recommended," and "supermarket."

[0594] Step 5:

[0595] The server generates a prompt based on the analysis results (extracted keywords and question text). The generated prompt is then sent to the generation AI (e.g., GPT-3). An example of a prompt is: "Please generate an appropriate answer to the following question: 'Please tell me the best supermarkets in Tokyo.'" The input is the analysis results (keyword list and question text), and the output is the generated prompt. Specifically, the server uses a prompt generation algorithm to construct a prompt and send it to the generation AI.

[0596] Step 6:

[0597] The generation AI generates an appropriate answer based on the prompt text. The generation AI receives the prompt text from the server and generates the answer text using an internal model. The input is the prompt text and the output is the answer text. Specifically, the generation AI goes through a generation process to generate an answer such as "A popular supermarket in Tokyo is XX supermarket" and sends this back to the server.

[0598] Step 7:

[0599] The server receives the generated answer and stores it in a database. It then sends the answer to the terminal and provides it to the user. The input is the answer text from the generation AI, and the output is the answer provided to the user. Specifically, the server creates a new answer record in the database, associates it with the user ID, and stores the answer text.

[0600] Step 8:

[0601] The user receives the response and checks its contents. They then enter feedback such as their level of satisfaction and areas for improvement. The device then sends the feedback information and the user ID to the server. The server then stores this feedback information in a database. The input is the user's feedback, and the output is the stored feedback data. Specifically, the server adds a feedback record to the database and associates it with the user ID.

[0602] Step 9:

[0603] The server uses the collected feedback as learning data to improve the performance of the generative AI. It analyzes the feedback information and adds it to the generative AI's training dataset. The input is user feedback, and the output is an updated training dataset. Specifically, the server analyzes the feedback data and provides new learning data to the generative AI.

[0604] Step 10:

[0605] If the question requires specialized knowledge, the server identifies an appropriate supporter from the supporter list and notifies the supporter of the support request. The input is the question content and analysis results, and the output is a request notification to the supporter. Specifically, the server searches the supporter list and sends a notification to the most suitable supporter.

[0606] Step 11:

[0607] The supporter receives the request and creates a specific answer. The answer is sent to the server via the terminal. The server stores the supporter's answer in a database and provides it to the user. The input is the answer created by the supporter, and the output is the answer provided to the user. In concrete terms, the server stores the received answer in a database and provides it to the user.

[0608] Step 12:

[0609] The server awards points to supporters based on user feedback. Points are calculated based on the quality of the answers and the user's satisfaction. The input is the feedback information, and the output is the awarded points. Specifically, the server calculates points using a feedback evaluation algorithm and adds them to the supporter's account.

[0610] Step 13:

[0611] Supporters use their terminals to check their points and request exchange for goods or services. The server accepts the request and carries out the necessary procedures. The input is the supporter's point exchange request, and the output is the provision of goods or services. Specifically, the server processes the exchange request and carries out the procedures to provide the supporter with goods or services.

[0612] (Application example 1)

[0613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] Conventional food delivery systems require users to manually search for restaurants and menus, making it difficult to find the right option in the process. They also lack a mechanism for providing appropriate answers to user questions, resulting in a poor user experience. Furthermore, there is no mechanism for actively using user feedback to train the generative AI and improve the accuracy of answers, making it difficult to improve the quality of the service. There is a need for a system that solves these problems, allows users to easily enjoy optimal food delivery services, and improves the overall quality of the service.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0616] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; and means for a user to input a specific question about food delivery and for the generation AI to suggest optimal stores and menus. This allows a user to receive optimal suggestions from the generation AI simply by inputting a specific question, and also enables the quality of service to be continuously improved through feedback.

[0617] "Generative AI" is an artificial intelligence technology that analyzes specific questions entered by users and generates optimal answers based on them.

[0618] A "support request" is request information sent to a supporter to ask for an answer to a user's question.

[0619] "Points" are units of virtual currency or credit awarded as a reward to supporters when they provide specific answers.

[0620] "Feedback" refers to information that a user returns through an application, such as their satisfaction with the answers provided, their ratings, or their opinions.

[0621] "Food delivery" is a system in which you order food and drinks and have them delivered to a designated location via a delivery service.

[0622] "User registration" is the process in which a user enters basic information such as name, email address, and password in order to use an application, and the server manages this information in a database.

[0623] A "supporter" is a person or system that has the ability to provide specialized knowledge and information in response to a user's questions.

[0624] The "server" is a central control device that receives input information and feedback from users and provides information to the generation AI and assistants.

[0625] A "database" is an information system that allows the server to centrally manage various data such as user information, questions, answers, feedback, and points.

[0626] "Means for suggesting optimal restaurants and menus" refers to a method that uses generative AI to recommend appropriate restaurants and dishes in response to specific food delivery-related questions from users.

[0627] The present invention is a system that improves the user experience in food delivery by using generative AI to provide optimal answers to specific questions entered by users. An embodiment of this system is described in detail below.

[0628] 1. User registration and initial settings

[0629] First, the user installs the application on their smartphone, smart glasses, or head-mounted display. When launching the application for the first time, the user enters basic information such as name, email address, and password on the account creation screen, which is then sent from the device to the server. The server then stores the user information in a database and generates a user ID. The user then selects a support category related to food delivery, and the device sends this information to the server. The server then stores the selected information in a database.

[0630] 2. Accepting support requests

[0631] The user inputs a specific question related to food delivery, such as "What do you recommend for dinner tonight?" The device then sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[0632] 3. Providing and Evaluating Answers

[0633] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user. For example, this answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here." The user checks the provided answer and enters feedback on the content. The device sends the feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0634] 4. Matching with supporters

[0635] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[0636] 5. Points allocation and management

[0637] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[0638] 6. Data accumulation and analysis

[0639] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0640] Hardware and Software Used

[0641] Hardware:

[0642] Smartphones (general smart devices)

[0643] Smart glasses (general-purpose smart wearable device)

[0644] Head-mounted display (general VR / AR device)

[0645] software:

[0646] Generative AI models (natural language processing models such as GPT-3)

[0647] Server-side database (MySQL, PostgreSQL)

[0648] Specific examples

[0649] User Ask: "What are your dinner recommendations for tonight?"

[0650] Generative AI response: "The nearby restaurant, Restaurant A, has a good reputation. You can make a reservation here."

[0651] In this way, the system of the present invention provides quick and appropriate answers to user questions about food delivery, improving the user experience and utilizing feedback to continuously improve the accuracy of the service.

[0652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0653] Step 1:

[0654] The user installs the application on their smartphone, smart glasses, or head-mounted display and launches it for the first time. The user enters basic information such as name, email address, and password on the account creation screen. The entered information is sent by the device to the server. The server stores the user information in a database and generates a user ID. The user selects a support category related to food delivery, and this information is also sent to the server and stored in the database.

[0655] Input: Name, Email Address, Password, Support Category

[0656] Output: User ID, user information stored in the database

[0657] Step 2:

[0658] Users input specific questions about food delivery into the app. For example, "What do you recommend for dinner tonight?" The input question and user ID are sent to the server by the device. The server analyzes the received question and extracts appropriate categories and keywords. The analysis results are then passed to the generation AI, which generates the optimal answer.

[0659] Input: Question, User ID

[0660] Output: Analysis results of the question, data passed to the generation AI

[0661] Step 3:

[0662] The generation AI understands the user's question and generates an appropriate answer. The generated answer is sent back to the server, which then provides the answer to the user. For example, the answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here."

[0663] Input: User question, analysis result of the generating AI

[0664] Output: Generated answer, information provided to the user

[0665] Step 4:

[0666] The user checks the answers provided and enters feedback on the content into the app. The feedback information is sent by the device to the server, which stores it in a database. The collected feedback is used as learning data for the generative AI.

[0667] Input: Feedback information

[0668] Output: Feedback stored in a database, training data for generative AI

[0669] Step 5:

[0670] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[0671] Input: Supporter list, support request

[0672] Output: Supporter's response, providing the response to the user

[0673] Step 6:

[0674] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[0675] Input: Supporter's answer, point rules

[0676] Output: Points awarded to supporters, product exchange procedure

[0677] Step 7:

[0678] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0679] Input: Outreach data

[0680] Output: Improved performance of generative AI, more accurate answers

[0681] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0682] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[0683] 1. User registration and initial settings

[0684] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0685] 2. Accepting support requests

[0686] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[0687] 3. Emotion Recognition and Answer Generation

[0688] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[0689] 4. Providing and Evaluating Answers

[0690] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0691] 5. Matching with supporters

[0692] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0693] 6. Points allocation and management

[0694] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0695] 7. Data accumulation and analysis

[0696] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0697] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improving the accuracy of support activities, and by combining it with an emotion engine, provides answers that take the user's emotions into consideration. In addition, a points system allows supporters to receive appropriate rewards, improving the accuracy and effectiveness of the entire support system.

[0698] The processing flow will be explained below.

[0699] Program processing steps

[0700] 1. User registration and initial settings

[0701] Step 1:

[0702] The user installs the application on the device and launches the application.

[0703] Step 2:

[0704] The user enters their name, email address, and password on the account creation screen.

[0705] The terminal transmits the input information to the server.

[0706] Step 3:

[0707] The server stores the user information in a database and generates a user ID.

[0708] Step 4:

[0709] The server returns the generated user ID to the terminal.

[0710] The terminal saves the user ID and moves to the next screen.

[0711] Step 5:

[0712] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0713] The terminal transmits the selected category information to the server.

[0714] Step 6:

[0715] The server stores the category information in a database.

[0716] 2. Accepting support requests

[0717] Step 7:

[0718] The user enters the specific question for which they need assistance.

[0719] The terminal sends the entered question and user ID to the server.

[0720] Step 8:

[0721] The server analyzes the content of the question using natural language processing technology.

[0722] Step 9:

[0723] The server extracts appropriate categories and keywords and passes them to the generation AI.

[0724] 3. Emotion Recognition and Answer Generation

[0725] Step 10:

[0726] The server sends the question content to the emotion engine.

[0727] The emotion engine recognizes the user's emotion based on the user's input.

[0728] Step 11:

[0729] The emotion engine returns the recognized emotion data to the server.

[0730] The server sends the emotion data to the generation AI.

[0731] Step 12:

[0732] The generative AI generates the optimal answer based on the question content and emotional data.

[0733] Step 13:

[0734] The generation AI sends the generated answer back to the server.

[0735] 4. Providing and Evaluating Answers

[0736] Step 14:

[0737] The server sends the answer received from the generation AI to the user.

[0738] Step 15:

[0739] The terminal receives the response and displays it to the user.

[0740] Step 16:

[0741] The user may enter their satisfaction with the answers provided and any additional feedback.

[0742] Step 17:

[0743] The terminal transmits the feedback information to the server.

[0744] Step 18:

[0745] The server stores the received feedback in a database and uses it as training data for the generative AI.

[0746] 5. Matching with supporters

[0747] Step 19:

[0748] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[0749] Step 20:

[0750] The server notifies the supporter of the support request.

[0751] Step 21:

[0752] The supporter receives the request, reviews the details, and creates a response.

[0753] Step 22:

[0754] The supporter's terminal transmits the created answer to the server.

[0755] Step 23:

[0756] The server provides the answers from the supporters to the user.

[0757] 6. Points allocation and management

[0758] Step 24:

[0759] If the supporter's answer is accepted, the server calculates points based on the point rules.

[0760] Step 25:

[0761] The server assigns points to the supporter's account.

[0762] Step 26:

[0763] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[0764] Step 27:

[0765] The supporter's terminal transmits this information to the server.

[0766] Step 28:

[0767] The server accepts the point exchange request and carries out the exchange procedure.

[0768] 7. Data accumulation and analysis

[0769] Step 29:

[0770] The server stores all support activity data (questions, answers, feedback, emotional data, etc.) in a database.

[0771] Step 30:

[0772] The generative AI uses this accumulated data to learn and improve its own performance.

[0773] summary

[0774] This series of steps allows for efficient support between users and supporters, and the generative AI provides optimal answers. Furthermore, by combining it with an emotion engine, answers can be provided that take the user's emotions into consideration. Furthermore, supporters can receive appropriate rewards through a point system, improving the accuracy and effectiveness of the overall support system.

[0775] Example 2

[0776] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0777] Conventional support systems have had the problem that the answers users receive to their questions are not always appropriate to the user's feelings or situation. Furthermore, the answers provided do not fully utilize the supporter's expertise, limiting the quality and effectiveness of the support. Furthermore, supporters are not properly compensated, which can lead to a decline in motivation for support activities.

[0778] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0779] In this invention, the server includes: a means for inputting a specific question for which a user requires assistance; a means for analyzing the question using a generation AI and generating an optimal answer; a means for providing the generated answer to the user and collecting feedback from the user; a means for training the generation AI based on the collected feedback; a means for recognizing the user's emotions based on the content of the question; a means for transmitting emotion data to the generation AI and generating an answer that takes emotions into consideration; a means for identifying a supporter and notifying the supporter of a support request; a means for awarding points based on the answer provided by the supporter; and a means for managing the points and allowing the supporter to exchange the points for goods or services. This enables the provision of answers that take the user's emotions into consideration and high-quality support by matching the user with supporters with specialized knowledge. Furthermore, the point system can provide appropriate rewards to supporters, thereby increasing their motivation for support activities.

[0780] "User" refers to a person who uses the system to seek assistance.

[0781] A "specific question requiring assistance" refers to a question that includes a specific problem or uncertainty that the user wants resolved.

[0782] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate the most appropriate answers.

[0783] An "emotion engine" refers to a system that recognizes a user's emotions based on the questions they enter and provides this as data to the generative AI.

[0784] "Feedback" refers to ratings and comments that users make on answers provided.

[0785] A "supporter" refers to a person who has specific knowledge and skills and whose role is to provide expert answers to users' questions.

[0786] "Points" refer to a form of reward given to a supporter when the supporter provides appropriate support to a user.

[0787] "Generated answer" refers to an answer generated by the generation AI based on the user's question.

[0788] "Notifying" refers to the act of informing relevant parties of specific information.

[0789] "Exchange" refers to the act of converting awarded points into concrete value such as goods or services.

[0790] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[0791] User registration and initial settings

[0792] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0793] Accepting support requests

[0794] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to a generative AI, which generates the optimal answer. The generative AI model used here is an advanced natural language processing model such as GPT-4.

[0795] Emotion Recognition and Answer Generation

[0796] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[0797] Providing and Evaluating Answers

[0798] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0799] Matching with supporters

[0800] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0801] Points allocation and management

[0802] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0803] Data accumulation and analysis

[0804] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0805] Specific examples

[0806] User: "What supermarkets in Tokyo do you recommend?"

[0807] Generative AI response: "Don't worry, Ameyoko Market is popular."

[0808] Prompt Sentence Examples

[0809] A user has asked, "What supermarkets are recommended in Tokyo?" You can sense the user's impatience. Please generate an answer that introduces appropriate supermarkets in a way that is considerate of the user.

[0810] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0811] Step 1: User registration and initial setup

[0812] Input: The user enters their name, email address, and password.

[0813] How it works: After installing the app, the user launches it for the first time and is taken to the account creation screen. The user enters their name, email address, and password.

[0814] Data processing: The terminal sends this information to the server, which stores the user information in a database and generates a user ID.

[0815] Output: The server returns the generated user ID to the terminal, and the user ID is displayed on the terminal.

[0816] Step 2: Select a support category

[0817] Input: The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0818] How it works: The user selects an assistance category in the settings screen within the app.

[0819] Data processing: The terminal sends the selected information to the server, which stores it in a database.

[0820] Output: The server returns a save completion message to the terminal, and the support category selected by the user is displayed on the terminal.

[0821] Step 3: Accepting a request for assistance

[0822] Input: The user enters a specific question. Example: "What supermarkets in Tokyo do you recommend?"

[0823] Operation: The user enters a question on the question screen and presses the send button.

[0824] Data processing: The device sends the question and user ID to the server. The server then analyzes the received question using natural language processing technology and extracts appropriate categories and keywords.

[0825] Output: The server passes the analysis results to the generation AI to obtain the optimal answer. The generated answer is saved on the server.

[0826] Step 4: Emotion recognition and answer generation

[0827] Input: The emotion engine receives the question. At the same time, the analysis results are sent to the generation AI.

[0828] How it works: The emotion engine analyzes the question and recognizes the user's emotions. For example, it analyzes the question "I need help urgently" and determines that the user is in a hurry.

[0829] Data processing: Emotional data is sent to the generation AI, which then generates an answer based on the emotional data and analysis results.

[0830] Output: The generative AI generates an answer that takes emotions into account, and this answer is sent back to the server.

[0831] Step 5: Provide answers and gather feedback

[0832] Input: Generated Answer

[0833] Operation: The server generates a response and sends it to the user's device. The user receives the response and checks its contents.

[0834] Data processing: User feedback is input and the device sends it to the server.

[0835] Output: The feedback stored on the server is recorded in a database and used as training data for the generative AI.

[0836] Step 6: Matching with donors

[0837] Input: Question content and user category information

[0838] How it works: The server reviews the question and determines if expertise is required. It then identifies an appropriate helper from the helper list and notifies them of the assistance request.

[0839] Data processing: After the supporter receives the request, they create a specific response and send it to the server.

[0840] Output: The server receives the supporter's answer and provides it to the user.

[0841] Step 7: Earn and manage your points

[0842] Input: Accepted supporter's answer, point rules

[0843] Action: The server awards points to the supporter.

[0844] Data processing: Points will be added to the supporter's account.

[0845] Output: Supporters can check their points and request to exchange them for goods or services through their terminal. The server accepts the exchange request and carries out the goods exchange procedure.

[0846] Step 8: Data collection and analysis

[0847] Input: All support activity data, including questions, answers, feedback, and emotional data

[0848] How it works: All data is stored on the server.

[0849] Data processing: The generative AI learns based on this accumulated data and aims to improve its performance.

[0850] Output: A more accurate answer is generated.

[0851] (Application example 2)

[0852] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0853] Conventional support systems have been inadequate in responding to users' emotional states and specific needs, leaving the improvement of user experience a challenge. Furthermore, in the content distribution service field, it has been difficult to accurately recommend content that users want to watch. This has made it difficult to provide the support users require quickly and appropriately.

[0854] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; means for recognizing the user's emotions using an emotion engine and generating an optimal answer taking those emotions into consideration; and means for recommending content that the user wants to view in a content distribution service. This makes it possible to provide appropriate assistance that takes the user's emotions and needs into consideration and accurately recommend content that the user wants to view.

[0855] A "user" is an individual who uses the support system and inputs a specific question for which an answer is sought.

[0856] A "supporter" is an individual or organization that has specialized knowledge and experience and provides answers to users' questions.

[0857] "Generative AI" is an artificial intelligence technology that analyzes input questions and generates optimal answers.

[0858] An "emotion engine" is a technology that recognizes emotions from user input and generates appropriate answers based on that.

[0859] "Feedback" refers to ratings and additional comments from users who receive answers.

[0860] "Points" are units within the system that are awarded as part of the reward to supporters and can be exchanged for goods and services.

[0861] A "content distribution service" is an online service that provides digital content such as movies, music, and books.

[0862] A "viewing history" is a record of content that a user has viewed in the past.

[0863] "Recommendation means" is a technology that selects and provides optimal content based on the user's questions, emotional state, viewing history, etc.

[0864] The "server" is a central system that manages data, analyzes questions, runs generative AI, operates the emotion engine, collects feedback, and manages points.

[0865] This invention is a system that allows users to input specific questions for which they need assistance and provides optimal answers by combining generative AI and an emotion engine. In particular, it includes a function for recommending content based on the user's viewing history and emotions in content distribution services.

[0866] 1. User registration and initial settings

[0867] First, the user installs the application on their smartphone and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which is then sent to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they require (e.g., movie recommendations, music recommendations), and the device sends this information to the server. The server saves the user's selection in a database.

[0868] 2. Accepting support requests

[0869] The user inputs a specific question for which they require assistance. For example, "I'm looking for a relaxing movie." The device sends the question and the user ID to the server. The server analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[0870] 3. Emotion Recognition and Answer Generation

[0871] The emotion engine recognizes emotions based on the user's input. For example, if a user sends a request such as "I feel like watching a funny movie lately," the emotion engine recognizes that the user is looking for relaxation and fun. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more in line with the user's needs. For example, a response such as "What do you think of the latest popular comedy movies?"

[0872] 4. Providing and Evaluating Answers

[0873] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0874] 5. Matching with supporters

[0875] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0876] 6. Points allocation and management

[0877] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0878] 7. Data accumulation and analysis

[0879] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0880] Specific examples

[0881] For example, if a user asks, "I'm tired these days, so I'm looking for a relaxing movie," the emotion engine recognizes the user's desire to relax. Based on that, the generative AI generates the answer, "How about a relaxing movie that's popular these days to help you relax?"

[0882] Prompt Sentence Examples

[0883] "I'm looking for a relaxing movie."

[0884] "I feel like watching a funny movie lately."

[0885] As described above, the embodiments of the invention are configured to understand the feelings and needs of the user and provide appropriate content and support.

[0886] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0887] Step 1:

[0888] A user installs the application on their smartphone and launches it for the first time. The user enters their name, email address, and password on the account creation screen, and the device sends this to the server. The server receives the user information as input data, generates a user ID, and stores this information in a database. The server generates a user ID as output data and sends it to the device.

[0889] Step 2:

[0890] The user enters a specific question for which they require assistance into the application. For example, they might enter a question like, "I'm looking for a relaxing movie." The device then sends the user ID and the question to the server. The server receives the user's question and ID as input data, analyzes the question using natural language processing technology, and extracts appropriate categories and keywords. The analyzed categories and keywords are then generated as output data and passed to the next processing step.

[0891] Step 3:

[0892] The server passes the analysis results to the generation AI, which generates the optimal answer. The generation AI receives the analyzed categories and keywords as input data and generates the optimal answer based on this. As a specific data calculation, it generates related answer candidates and selects the optimal one from among them. The generated answer is obtained as output data.

[0893] Step 4:

[0894] The emotion engine recognizes emotions based on the user's input. Specifically, it analyzes the text content of the user's input and uses an algorithm to determine the emotional state. It receives the user's question text as input data and generates the recognized emotional state as output data. The server sends the emotion data recognized by the emotion engine to the generation AI.

[0895] Step 5:

[0896] Generative AI takes emotional data into consideration to generate answers that are more in line with the user's needs. For example, it generates an answer such as, "How about watching a relaxing movie that's popular these days to soothe your fatigue?" It receives the emotion recognition results and question analysis results as input data, and adjusts the answer based on the emotion as data processing. It generates the final answer as output data.

[0897] Step 6:

[0898] The server provides the generated answer to the user. The server sends the answer received from the generation AI to the user's device, where it is received by the user. The server receives the generated answer as input data and sends it to the user's device as output data.

[0899] Step 7:

[0900] The user inputs their satisfaction with the answers provided and any additional feedback. The device then sends the feedback information to the server. The server receives the feedback as input data and stores it in a database. The feedback information is used as data for training the generative AI.

[0901] Step 8:

[0902] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server notifies the supporter of the support request and provides input data for the supporter to create a specific answer. The server then provides the supporter's answer to the user, delivering appropriate support to the user as output data.

[0903] Step 9:

[0904] Once the response is accepted, the server will award points to the supporter based on the point rules. The supporter will check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept this request and execute the goods exchange procedure. It will receive the supporter's point award information and exchange request as input data, and output confirmation of the goods exchange as output data.

[0905] Step 10:

[0906] The server stores all support activity data (question content, answers, feedback, emotional data, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity. The server receives the stored support activity data as input data and generates learning data as output data that contributes to improving the performance of the generating AI.

[0907] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0908] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0909] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0910] [Third embodiment]

[0911] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0912] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0913] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0914] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0915] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0916] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0917] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0918] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0919] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0920] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0921] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0922] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0923] The present invention provides an effective support system for both the supporter and the support recipient by allowing the user to input a specific question for which support is needed and having a generation AI provide the optimal answer. An embodiment of this system is described in detail below.

[0924] 1. User registration and initial settings

[0925] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[0926] 2. Accepting support requests

[0927] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[0928] 3. Providing and Evaluating Answers

[0929] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. For example, the answer might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the provided answer and enters feedback. The device sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[0930] 4. Matching with supporters

[0931] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[0932] 5. Points allocation and management

[0933] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[0934] 6. Data accumulation and analysis

[0935] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[0936] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improve the accuracy of support activities, and enable support activities to be pursued as a career or side job through a points system. In this way, we provide a system that improves the overall quality and effectiveness of support and contributes to society as a whole.

[0937] The processing flow will be explained below.

[0938] Program processing steps

[0939] 1. User registration and initial settings

[0940] Step 1:

[0941] The user installs the application on the device and launches the application.

[0942] Step 2:

[0943] The user enters their name, email address, and password on the account creation screen.

[0944] The terminal transmits the input information to the server.

[0945] Step 3:

[0946] The server stores the user information in a database and generates a user ID.

[0947] Step 4:

[0948] The server returns the generated user ID to the terminal.

[0949] The terminal saves the user ID and moves to the next screen.

[0950] Step 5:

[0951] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[0952] The terminal transmits the selected category information to the server.

[0953] Step 6:

[0954] The server stores the category information in a database.

[0955] 2. Accepting support requests

[0956] Step 7:

[0957] The user enters the specific question for which they need assistance.

[0958] The terminal sends the entered question and user ID to the server.

[0959] Step 8:

[0960] The server analyzes the content of the question using natural language processing technology.

[0961] The server extracts appropriate categories and keywords and passes them to the generation AI.

[0962] 3. Generate and provide answers

[0963] Step 9:

[0964] The generative AI generates the optimal answer based on the question it receives.

[0965] The generation AI sends the generated answer back to the server.

[0966] Step 10:

[0967] The server sends the answer received from the generation AI to the user.

[0968] The terminal receives the response and displays it to the user.

[0969] 4. User Feedback

[0970] Step 11:

[0971] The user may enter their satisfaction with the answers provided and any additional feedback.

[0972] The terminal transmits the feedback information to the server.

[0973] Step 12:

[0974] The server stores the received feedback in a database and uses it as training data for the generative AI.

[0975] 5. Matching with supporters (if necessary)

[0976] Step 13:

[0977] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[0978] Step 14:

[0979] The server notifies the supporter of the support request.

[0980] Step 15:

[0981] The supporter receives the request, reviews the details, and creates a response.

[0982] Step 16:

[0983] The supporter's terminal transmits the created answer to the server.

[0984] Step 17:

[0985] The server provides the answers from the supporters to the user.

[0986] 6. Points allocation and management

[0987] Step 18:

[0988] If the supporter's answer is accepted, the server calculates points based on the point rules.

[0989] The server assigns points to the supporter's account.

[0990] Step 19:

[0991] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[0992] The supporter's terminal transmits this information to the server.

[0993] Step 20:

[0994] The server accepts the point exchange request and carries out the exchange procedure.

[0995] summary

[0996] This series of steps allows for efficient support between users and supporters, and optimal answers are provided using generative AI. Supporters can also receive appropriate rewards through a points system, improving the accuracy and effectiveness of the overall support system.

[0997] Example 1

[0998] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0999] To address today's diverse support needs, systems are needed that allow users to easily input specific questions and quickly provide optimal answers. However, existing support systems have the following problems. First, they lack a process for clarifying the category of support a user needs, making it difficult to provide accurate support. Furthermore, there is no established method for collecting and utilizing feedback to improve the quality of answers generated by generative AI, resulting in low accuracy of answers. Finally, there is a lack of appropriate incentives for supporters, making it difficult for them to continue participating.

[1000] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1001] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and for the supporter to exchange the points for goods or services; means for installing an application on a terminal and transmitting a name, email address, and password to the server; means for the user to select a category for which assistance is required and transmit that information to the server; means for the server to accumulate user information and feedback in a database; and means for the generation AI to train to improve its performance based on the accumulated data. This enables the system to accurately and efficiently provide the assistance required by the user, improve the accuracy of the generation AI's answers, and provide appropriate incentives to supporters, thereby achieving continuous and effective assistance.

[1002] "User" refers to an individual or group of people who require assistance and utilize the system to input a specific question.

[1003] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate optimal answers.

[1004] A "server" refers to a computer system that receives and stores information from users, interacts with the generating AI, and notifies supporters of requests.

[1005] "Supporter" refers to an individual or group who uses their specialized knowledge to provide answers to users' questions and earn points.

[1006] A "database" refers to a storage device for storing and managing user information, support activity data, feedback, etc.

[1007] "Points" refer to the rewards given to supporters for the answers they provide, and are units within the system that can be accumulated and exchanged for goods and services.

[1008] "Feedback" refers to ratings and comments that users make on answers provided.

[1009] "Installation" refers to the process of placing an application on a device and making it available for use.

[1010] A "question" refers to a sentence or group of sentences that a user inputs to the system with the specific matter for which they require assistance.

[1011] A "category" refers to a group for classifying the area of ​​support that a user needs.

[1012] A "prompt" refers to a question or instruction used to give appropriate instructions to the generative AI.

[1013] MODE FOR CARRYING OUT THE INVENTION

[1014] The present invention is a support system in which a user inputs a specific question, and a generation AI analyzes the question and provides the most appropriate answer. This system is composed of a user, a terminal, a server, and a generation AI. Detailed embodiments are described below.

[1015] User registration and initial settings

[1016] First, the user installs the application on their device. Once the installation is complete, the user launches the application and enters their name, email address, and password on the account creation screen. The device sends this information to the server. The server stores the received information in a database and generates a unique user ID. The generated user ID is sent back to the device. Next, the user selects the category for which they need assistance and sends this information to the server. The server stores the selected information in a database.

[1017] Accepting support requests

[1018] The user inputs a specific question for which they require assistance. For example, they can input a question such as, "What supermarkets in Tokyo are recommended?" The device then sends this question and the user ID to the server. The server stores the received question in a database and analyzes it using natural language processing (NLP) technology. Specifically, it uses libraries such as spaCy to extract appropriate categories and keywords from the question. The server then passes the analysis results to a generative AI, which generates a prompt. An example of a prompt is, "Generate an appropriate answer to the following question: 'What supermarkets in Tokyo are recommended?'"

[1019] Providing and Evaluating Answers

[1020] The generative AI analyzes the question based on the prompt text and generates an appropriate answer. The generated answer is sent back to the server and stored in a database. The server then sends the answer to the device and provides it to the user. The user checks the answer and enters feedback. For example, the answer provided might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the answer and sends feedback information to the server via their device. The server stores the feedback in a database and uses this data to learn and improve the performance of the generative AI.

[1021] Matching with supporters

[1022] If the question requires specialized knowledge, the server identifies an appropriate assistant from the list of assistants. The server notifies the assistant of the request for assistance, and the assistant receives the request. The assistant creates a specific answer and sends it to the server via the terminal. The server stores the answer in a database and provides it to the user.

[1023] Points allocation and management

[1024] The server awards points to supporters based on feedback from users. Points are calculated according to the quality of answers and the user's satisfaction. Supporters can check their points and request exchange for goods or services via their terminals. The server accepts the request and carries out the necessary procedures.

[1025] Data accumulation and analysis

[1026] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this data to learn and improve its performance. This enables it to provide more accurate answers in future support activities.

[1027] This system allows users to receive accurate and prompt assistance, and the helpers can earn points as an incentive. The generative AI can improve its performance through feedback, thereby increasing the overall quality of assistance.

[1028] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1029] Step 1:

[1030] The user installs the application on their device. After installation, they launch the application and enter their name, email address, and password on the account creation screen. The device sends this input data to the server. The server stores the received user information in a database, generates a unique user ID, and sends it back to the device. Specifically, the server creates a new record in the database, stores the name, email address, and password, and simultaneously generates an incrementing user ID.

[1031] Step 2:

[1032] The user selects the category for which they need assistance (e.g., general life, exams, employment). The device sends the selected category information along with the user ID to the server. The server saves this information in a database. Specifically, the server associates the selected category information with the user ID generated earlier in the database and stores it.

[1033] Step 3:

[1034] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device sends the question and the user ID to the server. The server then stores the received question in a database. Specifically, the server creates a new question record in the database and stores the question text and the user ID.

[1035] Step 4:

[1036] The server analyzes the received question using natural language processing (NLP) technology. For example, it uses spaCy to tokenize the text and extract keywords. The input is the question text, and the output is a list of extracted keywords. For example, the server analyzes the question "What supermarkets are recommended in Tokyo?" and extracts keywords such as "Tokyo," "recommended," and "supermarket."

[1037] Step 5:

[1038] The server generates a prompt based on the analysis results (extracted keywords and question text). The generated prompt is then sent to the generation AI (e.g., GPT-3). An example of a prompt is: "Please generate an appropriate answer to the following question: 'Please tell me the best supermarkets in Tokyo.'" The input is the analysis results (keyword list and question text), and the output is the generated prompt. Specifically, the server uses a prompt generation algorithm to construct a prompt and send it to the generation AI.

[1039] Step 6:

[1040] The generation AI generates an appropriate answer based on the prompt text. The generation AI receives the prompt text from the server and generates the answer text using an internal model. The input is the prompt text and the output is the answer text. Specifically, the generation AI goes through a generation process to generate an answer such as "A popular supermarket in Tokyo is XX supermarket" and sends this back to the server.

[1041] Step 7:

[1042] The server receives the generated answer and stores it in a database. It then sends the answer to the terminal and provides it to the user. The input is the answer text from the generation AI, and the output is the answer provided to the user. Specifically, the server creates a new answer record in the database, associates it with the user ID, and stores the answer text.

[1043] Step 8:

[1044] The user receives the response and checks its contents. They then enter feedback such as their level of satisfaction and areas for improvement. The device then sends the feedback information and the user ID to the server. The server then stores this feedback information in a database. The input is the user's feedback, and the output is the stored feedback data. Specifically, the server adds a feedback record to the database and associates it with the user ID.

[1045] Step 9:

[1046] The server uses the collected feedback as learning data to improve the performance of the generative AI. It analyzes the feedback information and adds it to the generative AI's training dataset. The input is user feedback, and the output is an updated training dataset. Specifically, the server analyzes the feedback data and provides new learning data to the generative AI.

[1047] Step 10:

[1048] If the question requires specialized knowledge, the server identifies an appropriate supporter from the supporter list and notifies the supporter of the support request. The input is the question content and analysis results, and the output is a request notification to the supporter. Specifically, the server searches the supporter list and sends a notification to the most suitable supporter.

[1049] Step 11:

[1050] The supporter receives the request and creates a specific answer. The answer is sent to the server via the terminal. The server stores the supporter's answer in a database and provides it to the user. The input is the answer created by the supporter, and the output is the answer provided to the user. In concrete terms, the server stores the received answer in a database and provides it to the user.

[1051] Step 12:

[1052] The server awards points to supporters based on user feedback. Points are calculated based on the quality of the answers and the user's satisfaction. The input is the feedback information, and the output is the awarded points. Specifically, the server calculates points using a feedback evaluation algorithm and adds them to the supporter's account.

[1053] Step 13:

[1054] Supporters use their terminals to check their points and request exchange for goods or services. The server accepts the request and carries out the necessary procedures. The input is the supporter's point exchange request, and the output is the provision of goods or services. Specifically, the server processes the exchange request and carries out the procedures to provide the supporter with goods or services.

[1055] (Application example 1)

[1056] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1057] Conventional food delivery systems require users to manually search for restaurants and menus, making it difficult to find the right option in the process. They also lack a mechanism for providing appropriate answers to user questions, resulting in a poor user experience. Furthermore, there is no mechanism for actively using user feedback to train the generative AI and improve the accuracy of answers, making it difficult to improve the quality of the service. There is a need for a system that solves these problems, allows users to easily enjoy optimal food delivery services, and improves the overall quality of the service.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1059] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; and means for a user to input a specific question about food delivery and for the generation AI to suggest optimal stores and menus. This allows a user to receive optimal suggestions from the generation AI simply by inputting a specific question, and also enables the quality of service to be continuously improved through feedback.

[1060] "Generative AI" is an artificial intelligence technology that analyzes specific questions entered by users and generates optimal answers based on them.

[1061] A "support request" is request information sent to a supporter to ask for an answer to a user's question.

[1062] "Points" are units of virtual currency or credit awarded as a reward to supporters when they provide specific answers.

[1063] "Feedback" refers to information that a user returns through an application, such as their satisfaction with the answers provided, their ratings, or their opinions.

[1064] "Food delivery" is a system in which you order food and drinks and have them delivered to a designated location via a delivery service.

[1065] "User registration" is the process in which a user enters basic information such as name, email address, and password in order to use an application, and the server manages this information in a database.

[1066] A "supporter" is a person or system that has the ability to provide specialized knowledge and information in response to a user's questions.

[1067] The "server" is a central control device that receives input information and feedback from users and provides information to the generation AI and assistants.

[1068] A "database" is an information system that allows the server to centrally manage various data such as user information, questions, answers, feedback, and points.

[1069] "Means for suggesting optimal restaurants and menus" refers to a method that uses generative AI to recommend appropriate restaurants and dishes in response to specific food delivery-related questions from users.

[1070] The present invention is a system that improves the user experience in food delivery by using generative AI to provide optimal answers to specific questions entered by users. An embodiment of this system is described in detail below.

[1071] 1. User registration and initial settings

[1072] First, the user installs the application on their smartphone, smart glasses, or head-mounted display. When launching the application for the first time, the user enters basic information such as name, email address, and password on the account creation screen, which is then sent from the device to the server. The server then stores the user information in a database and generates a user ID. The user then selects a support category related to food delivery, and the device sends this information to the server. The server then stores the selected information in a database.

[1073] 2. Accepting support requests

[1074] The user inputs a specific question related to food delivery, such as "What do you recommend for dinner tonight?" The device then sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[1075] 3. Providing and Evaluating Answers

[1076] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user. For example, this answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here." The user checks the provided answer and enters feedback on the content. The device sends the feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1077] 4. Matching with supporters

[1078] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[1079] 5. Points allocation and management

[1080] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[1081] 6. Data accumulation and analysis

[1082] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1083] Hardware and Software Used

[1084] Hardware:

[1085] Smartphones (general smart devices)

[1086] Smart glasses (general-purpose smart wearable device)

[1087] Head-mounted display (general VR / AR device)

[1088] software:

[1089] Generative AI models (natural language processing models such as GPT-3)

[1090] Server-side database (MySQL, PostgreSQL)

[1091] Specific examples

[1092] User Ask: "What are your dinner recommendations for tonight?"

[1093] Generative AI response: "The nearby restaurant, Restaurant A, has a good reputation. You can make a reservation here."

[1094] In this way, the system of the present invention provides quick and appropriate answers to user questions about food delivery, improving the user experience and utilizing feedback to continuously improve the accuracy of the service.

[1095] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1096] Step 1:

[1097] The user installs the application on their smartphone, smart glasses, or head-mounted display and launches it for the first time. The user enters basic information such as name, email address, and password on the account creation screen. The entered information is sent by the device to the server. The server stores the user information in a database and generates a user ID. The user selects a support category related to food delivery, and this information is also sent to the server and stored in the database.

[1098] Input: Name, Email Address, Password, Support Category

[1099] Output: User ID, user information stored in the database

[1100] Step 2:

[1101] Users input specific questions about food delivery into the app. For example, "What do you recommend for dinner tonight?" The input question and user ID are sent to the server by the device. The server analyzes the received question and extracts appropriate categories and keywords. The analysis results are then passed to the generation AI, which generates the optimal answer.

[1102] Input: Question, User ID

[1103] Output: Analysis results of the question, data passed to the generation AI

[1104] Step 3:

[1105] The generation AI understands the user's question and generates an appropriate answer. The generated answer is sent back to the server, which then provides the answer to the user. For example, the answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here."

[1106] Input: User question, analysis result of the generating AI

[1107] Output: Generated answer, information provided to the user

[1108] Step 4:

[1109] The user checks the answers provided and enters feedback on the content into the app. The feedback information is sent by the device to the server, which stores it in a database. The collected feedback is used as learning data for the generative AI.

[1110] Input: Feedback information

[1111] Output: Feedback stored in a database, training data for generative AI

[1112] Step 5:

[1113] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[1114] Input: Supporter list, support request

[1115] Output: Supporter's response, providing the response to the user

[1116] Step 6:

[1117] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[1118] Input: Supporter's answer, point rules

[1119] Output: Points awarded to supporters, product exchange procedure

[1120] Step 7:

[1121] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1122] Input: Outreach data

[1123] Output: Improved performance of generative AI, more accurate answers

[1124] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1125] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[1126] 1. User registration and initial settings

[1127] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[1128] 2. Accepting support requests

[1129] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[1130] 3. Emotion Recognition and Answer Generation

[1131] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[1132] 4. Providing and Evaluating Answers

[1133] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1134] 5. Matching with supporters

[1135] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1136] 6. Points allocation and management

[1137] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1138] 7. Data accumulation and analysis

[1139] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1140] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improving the accuracy of support activities, and by combining it with an emotion engine, provides answers that take the user's emotions into consideration. In addition, a points system allows supporters to receive appropriate rewards, improving the accuracy and effectiveness of the entire support system.

[1141] The processing flow will be explained below.

[1142] Program processing steps

[1143] 1. User registration and initial settings

[1144] Step 1:

[1145] The user installs the application on the device and launches the application.

[1146] Step 2:

[1147] The user enters their name, email address, and password on the account creation screen.

[1148] The terminal transmits the input information to the server.

[1149] Step 3:

[1150] The server stores the user information in a database and generates a user ID.

[1151] Step 4:

[1152] The server returns the generated user ID to the terminal.

[1153] The terminal saves the user ID and moves to the next screen.

[1154] Step 5:

[1155] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[1156] The terminal transmits the selected category information to the server.

[1157] Step 6:

[1158] The server stores the category information in a database.

[1159] 2. Accepting support requests

[1160] Step 7:

[1161] The user enters the specific question for which they need assistance.

[1162] The terminal sends the entered question and user ID to the server.

[1163] Step 8:

[1164] The server analyzes the content of the question using natural language processing technology.

[1165] Step 9:

[1166] The server extracts appropriate categories and keywords and passes them to the generation AI.

[1167] 3. Emotion Recognition and Answer Generation

[1168] Step 10:

[1169] The server sends the question content to the emotion engine.

[1170] The emotion engine recognizes the user's emotion based on the user's input.

[1171] Step 11:

[1172] The emotion engine returns the recognized emotion data to the server.

[1173] The server sends the emotion data to the generation AI.

[1174] Step 12:

[1175] The generative AI generates the optimal answer based on the question content and emotional data.

[1176] Step 13:

[1177] The generation AI sends the generated answer back to the server.

[1178] 4. Providing and Evaluating Answers

[1179] Step 14:

[1180] The server sends the answer received from the generation AI to the user.

[1181] Step 15:

[1182] The terminal receives the response and displays it to the user.

[1183] Step 16:

[1184] The user may enter their satisfaction with the answers provided and any additional feedback.

[1185] Step 17:

[1186] The terminal transmits the feedback information to the server.

[1187] Step 18:

[1188] The server stores the received feedback in a database and uses it as training data for the generative AI.

[1189] 5. Matching with supporters

[1190] Step 19:

[1191] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[1192] Step 20:

[1193] The server notifies the supporter of the support request.

[1194] Step 21:

[1195] The supporter receives the request, reviews the details, and creates a response.

[1196] Step 22:

[1197] The supporter's terminal transmits the created answer to the server.

[1198] Step 23:

[1199] The server provides the answers from the supporters to the user.

[1200] 6. Points allocation and management

[1201] Step 24:

[1202] If the supporter's answer is accepted, the server calculates points based on the point rules.

[1203] Step 25:

[1204] The server assigns points to the supporter's account.

[1205] Step 26:

[1206] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[1207] Step 27:

[1208] The supporter's terminal transmits this information to the server.

[1209] Step 28:

[1210] The server accepts the point exchange request and carries out the exchange procedure.

[1211] 7. Data accumulation and analysis

[1212] Step 29:

[1213] The server stores all support activity data (questions, answers, feedback, emotional data, etc.) in a database.

[1214] Step 30:

[1215] The generative AI uses this accumulated data to learn and improve its own performance.

[1216] summary

[1217] This series of steps allows for efficient support between users and supporters, and the generative AI provides optimal answers. Furthermore, by combining it with an emotion engine, answers can be provided that take the user's emotions into consideration. Furthermore, supporters can receive appropriate rewards through a point system, improving the accuracy and effectiveness of the overall support system.

[1218] Example 2

[1219] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1220] Conventional support systems have had the problem that the answers users receive to their questions are not always appropriate to the user's feelings or situation. Furthermore, the answers provided do not fully utilize the supporter's expertise, limiting the quality and effectiveness of the support. Furthermore, supporters are not properly compensated, which can lead to a decline in motivation for support activities.

[1221] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1222] In this invention, the server includes: a means for inputting a specific question for which a user requires assistance; a means for analyzing the question using a generation AI and generating an optimal answer; a means for providing the generated answer to the user and collecting feedback from the user; a means for training the generation AI based on the collected feedback; a means for recognizing the user's emotions based on the content of the question; a means for transmitting emotion data to the generation AI and generating an answer that takes emotions into consideration; a means for identifying a supporter and notifying the supporter of a support request; a means for awarding points based on the answer provided by the supporter; and a means for managing the points and allowing the supporter to exchange the points for goods or services. This enables the provision of answers that take the user's emotions into consideration and high-quality support by matching the user with supporters with specialized knowledge. Furthermore, the point system can provide appropriate rewards to supporters, thereby increasing their motivation for support activities.

[1223] "User" refers to a person who uses the system to seek assistance.

[1224] A "specific question requiring assistance" refers to a question that includes a specific problem or uncertainty that the user wants resolved.

[1225] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate the most appropriate answers.

[1226] An "emotion engine" refers to a system that recognizes a user's emotions based on the questions they enter and provides this as data to the generative AI.

[1227] "Feedback" refers to ratings and comments that users make on answers provided.

[1228] A "supporter" refers to a person who has specific knowledge and skills and whose role is to provide expert answers to users' questions.

[1229] "Points" refer to a form of reward given to a supporter when the supporter provides appropriate support to a user.

[1230] "Generated answer" refers to an answer generated by the generation AI based on the user's question.

[1231] "Notifying" refers to the act of informing relevant parties of specific information.

[1232] "Exchange" refers to the act of converting awarded points into concrete value such as goods or services.

[1233] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[1234] User registration and initial settings

[1235] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[1236] Accepting support requests

[1237] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to a generative AI, which generates the optimal answer. The generative AI model used here is an advanced natural language processing model such as GPT-4.

[1238] Emotion Recognition and Answer Generation

[1239] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[1240] Providing and Evaluating Answers

[1241] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1242] Matching with supporters

[1243] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1244] Points allocation and management

[1245] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1246] Data accumulation and analysis

[1247] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1248] Specific examples

[1249] User: "What supermarkets in Tokyo do you recommend?"

[1250] Generative AI response: "Don't worry, Ameyoko Market is popular."

[1251] Prompt Sentence Examples

[1252] A user has asked, "What supermarkets are recommended in Tokyo?" You can sense the user's impatience. Please generate an answer that introduces appropriate supermarkets in a way that is considerate of the user.

[1253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1254] Step 1: User registration and initial setup

[1255] Input: The user enters their name, email address, and password.

[1256] How it works: After installing the app, the user launches it for the first time and is taken to the account creation screen. The user enters their name, email address, and password.

[1257] Data processing: The terminal sends this information to the server, which stores the user information in a database and generates a user ID.

[1258] Output: The server returns the generated user ID to the terminal, and the user ID is displayed on the terminal.

[1259] Step 2: Select a support category

[1260] Input: The user selects the category for which they need assistance (e.g., general life, exams, employment).

[1261] How it works: The user selects an assistance category in the settings screen within the app.

[1262] Data processing: The terminal sends the selected information to the server, which stores it in a database.

[1263] Output: The server returns a save completion message to the terminal, and the support category selected by the user is displayed on the terminal.

[1264] Step 3: Accepting a request for assistance

[1265] Input: The user enters a specific question. Example: "What supermarkets in Tokyo do you recommend?"

[1266] Operation: The user enters a question on the question screen and presses the send button.

[1267] Data processing: The device sends the question and user ID to the server. The server then analyzes the received question using natural language processing technology and extracts appropriate categories and keywords.

[1268] Output: The server passes the analysis results to the generation AI to obtain the optimal answer. The generated answer is saved on the server.

[1269] Step 4: Emotion recognition and answer generation

[1270] Input: The emotion engine receives the question. At the same time, the analysis results are sent to the generation AI.

[1271] How it works: The emotion engine analyzes the question and recognizes the user's emotions. For example, it analyzes the question "I need help urgently" and determines that the user is in a hurry.

[1272] Data processing: Emotional data is sent to the generation AI, which then generates an answer based on the emotional data and analysis results.

[1273] Output: The generative AI generates an answer that takes emotions into account, and this answer is sent back to the server.

[1274] Step 5: Provide answers and gather feedback

[1275] Input: Generated Answer

[1276] Operation: The server generates a response and sends it to the user's device. The user receives the response and checks its contents.

[1277] Data processing: User feedback is input and the device sends it to the server.

[1278] Output: The feedback stored on the server is recorded in a database and used as training data for the generative AI.

[1279] Step 6: Matching with donors

[1280] Input: Question content and user category information

[1281] How it works: The server reviews the question and determines if expertise is required. It then identifies an appropriate helper from the helper list and notifies them of the assistance request.

[1282] Data processing: After the supporter receives the request, they create a specific response and send it to the server.

[1283] Output: The server receives the supporter's answer and provides it to the user.

[1284] Step 7: Earn and manage your points

[1285] Input: Accepted supporter's answer, point rules

[1286] Action: The server awards points to the supporter.

[1287] Data processing: Points will be added to the supporter's account.

[1288] Output: Supporters can check their points and request to exchange them for goods or services through their terminal. The server accepts the exchange request and carries out the goods exchange procedure.

[1289] Step 8: Data collection and analysis

[1290] Input: All support activity data, including questions, answers, feedback, and emotional data

[1291] How it works: All data is stored on the server.

[1292] Data processing: The generative AI learns based on this accumulated data and aims to improve its performance.

[1293] Output: A more accurate answer is generated.

[1294] (Application example 2)

[1295] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1296] Conventional support systems have been inadequate in responding to users' emotional states and specific needs, leaving the improvement of user experience a challenge. Furthermore, in the content distribution service field, it has been difficult to accurately recommend content that users want to watch. This has made it difficult to provide the support users require quickly and appropriately.

[1297] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; means for recognizing the user's emotions using an emotion engine and generating an optimal answer taking those emotions into consideration; and means for recommending content that the user wants to view in a content distribution service. This makes it possible to provide appropriate assistance that takes the user's emotions and needs into consideration and accurately recommend content that the user wants to view.

[1298] A "user" is an individual who uses the support system and inputs a specific question for which an answer is sought.

[1299] A "supporter" is an individual or organization that has specialized knowledge and experience and provides answers to users' questions.

[1300] "Generative AI" is an artificial intelligence technology that analyzes input questions and generates optimal answers.

[1301] An "emotion engine" is a technology that recognizes emotions from user input and generates appropriate answers based on that.

[1302] "Feedback" refers to ratings and additional comments from users who receive answers.

[1303] "Points" are units within the system that are awarded as part of the reward to supporters and can be exchanged for goods and services.

[1304] A "content distribution service" is an online service that provides digital content such as movies, music, and books.

[1305] A "viewing history" is a record of content that a user has viewed in the past.

[1306] "Recommendation means" is a technology that selects and provides optimal content based on the user's questions, emotional state, viewing history, etc.

[1307] The "server" is a central system that manages data, analyzes questions, runs generative AI, operates the emotion engine, collects feedback, and manages points.

[1308] This invention is a system that allows users to input specific questions for which they need assistance and provides optimal answers by combining generative AI and an emotion engine. In particular, it includes a function for recommending content based on the user's viewing history and emotions in content distribution services.

[1309] 1. User registration and initial settings

[1310] First, the user installs the application on their smartphone and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which is then sent to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they require (e.g., movie recommendations, music recommendations), and the device sends this information to the server. The server saves the user's selection in a database.

[1311] 2. Accepting support requests

[1312] The user inputs a specific question for which they require assistance. For example, "I'm looking for a relaxing movie." The device sends the question and the user ID to the server. The server analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[1313] 3. Emotion Recognition and Answer Generation

[1314] The emotion engine recognizes emotions based on the user's input. For example, if a user sends a request such as "I feel like watching a funny movie lately," the emotion engine recognizes that the user is looking for relaxation and fun. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more in line with the user's needs. For example, a response such as "What do you think of the latest popular comedy movies?"

[1315] 4. Providing and Evaluating Answers

[1316] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1317] 5. Matching with supporters

[1318] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1319] 6. Points allocation and management

[1320] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1321] 7. Data accumulation and analysis

[1322] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1323] Specific examples

[1324] For example, if a user asks, "I'm tired these days, so I'm looking for a relaxing movie," the emotion engine recognizes the user's desire to relax. Based on that, the generative AI generates the answer, "How about a relaxing movie that's popular these days to help you relax?"

[1325] Prompt Sentence Examples

[1326] "I'm looking for a relaxing movie."

[1327] "I feel like watching a funny movie lately."

[1328] As described above, the embodiments of the invention are configured to understand the feelings and needs of the user and provide appropriate content and support.

[1329] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1330] Step 1:

[1331] A user installs the application on their smartphone and launches it for the first time. The user enters their name, email address, and password on the account creation screen, and the device sends this to the server. The server receives the user information as input data, generates a user ID, and stores this information in a database. The server generates a user ID as output data and sends it to the device.

[1332] Step 2:

[1333] The user enters a specific question for which they require assistance into the application. For example, they might enter a question like, "I'm looking for a relaxing movie." The device then sends the user ID and the question to the server. The server receives the user's question and ID as input data, analyzes the question using natural language processing technology, and extracts appropriate categories and keywords. The analyzed categories and keywords are then generated as output data and passed to the next processing step.

[1334] Step 3:

[1335] The server passes the analysis results to the generation AI, which generates the optimal answer. The generation AI receives the analyzed categories and keywords as input data and generates the optimal answer based on this. As a specific data calculation, it generates related answer candidates and selects the optimal one from among them. The generated answer is obtained as output data.

[1336] Step 4:

[1337] The emotion engine recognizes emotions based on the user's input. Specifically, it analyzes the text content of the user's input and uses an algorithm to determine the emotional state. It receives the user's question text as input data and generates the recognized emotional state as output data. The server sends the emotion data recognized by the emotion engine to the generation AI.

[1338] Step 5:

[1339] Generative AI takes emotional data into consideration to generate answers that are more in line with the user's needs. For example, it generates an answer such as, "How about watching a relaxing movie that's popular these days to soothe your fatigue?" It receives the emotion recognition results and question analysis results as input data, and adjusts the answer based on the emotion as data processing. It generates the final answer as output data.

[1340] Step 6:

[1341] The server provides the generated answer to the user. The server sends the answer received from the generation AI to the user's device, where it is received by the user. The server receives the generated answer as input data and sends it to the user's device as output data.

[1342] Step 7:

[1343] The user inputs their satisfaction with the answers provided and any additional feedback. The device then sends the feedback information to the server. The server receives the feedback as input data and stores it in a database. The feedback information is used as data for training the generative AI.

[1344] Step 8:

[1345] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server notifies the supporter of the support request and provides input data for the supporter to create a specific answer. The server then provides the supporter's answer to the user, delivering appropriate support to the user as output data.

[1346] Step 9:

[1347] Once the response is accepted, the server will award points to the supporter based on the point rules. The supporter will check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept this request and execute the goods exchange procedure. It will receive the supporter's point award information and exchange request as input data, and output confirmation of the goods exchange as output data.

[1348] Step 10:

[1349] The server stores all support activity data (question content, answers, feedback, emotional data, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity. The server receives the stored support activity data as input data and generates learning data as output data that contributes to improving the performance of the generating AI.

[1350] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1351] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1352] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1353] [Fourth embodiment]

[1354] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1355] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1356] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1357] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1358] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1359] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1360] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1361] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1362] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1363] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1364] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1365] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1366] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1367] The present invention provides an effective support system for both the supporter and the support recipient by allowing the user to input a specific question for which support is needed and having a generation AI provide the optimal answer. An embodiment of this system is described in detail below.

[1368] 1. User registration and initial settings

[1369] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[1370] 2. Accepting support requests

[1371] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[1372] 3. Providing and Evaluating Answers

[1373] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. For example, the answer might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the provided answer and enters feedback. The device sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1374] 4. Matching with supporters

[1375] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1376] 5. Points allocation and management

[1377] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1378] 6. Data accumulation and analysis

[1379] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1380] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improve the accuracy of support activities, and enable support activities to be pursued as a career or side job through a points system. In this way, we provide a system that improves the overall quality and effectiveness of support and contributes to society as a whole.

[1381] The processing flow will be explained below.

[1382] Program processing steps

[1383] 1. User registration and initial settings

[1384] Step 1:

[1385] The user installs the application on the device and launches the application.

[1386] Step 2:

[1387] The user enters their name, email address, and password on the account creation screen.

[1388] The terminal transmits the input information to the server.

[1389] Step 3:

[1390] The server stores the user information in a database and generates a user ID.

[1391] Step 4:

[1392] The server returns the generated user ID to the terminal.

[1393] The terminal saves the user ID and moves to the next screen.

[1394] Step 5:

[1395] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[1396] The terminal transmits the selected category information to the server.

[1397] Step 6:

[1398] The server stores the category information in a database.

[1399] 2. Accepting support requests

[1400] Step 7:

[1401] The user enters the specific question for which they need assistance.

[1402] The terminal sends the entered question and user ID to the server.

[1403] Step 8:

[1404] The server analyzes the content of the question using natural language processing technology.

[1405] The server extracts appropriate categories and keywords and passes them to the generation AI.

[1406] 3. Generate and provide answers

[1407] Step 9:

[1408] The generative AI generates the optimal answer based on the question it receives.

[1409] The generation AI sends the generated answer back to the server.

[1410] Step 10:

[1411] The server sends the answer received from the generation AI to the user.

[1412] The terminal receives the response and displays it to the user.

[1413] 4. User Feedback

[1414] Step 11:

[1415] The user may enter their satisfaction with the answers provided and any additional feedback.

[1416] The terminal transmits the feedback information to the server.

[1417] Step 12:

[1418] The server stores the received feedback in a database and uses it as training data for the generative AI.

[1419] 5. Matching with supporters (if necessary)

[1420] Step 13:

[1421] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[1422] Step 14:

[1423] The server notifies the supporter of the support request.

[1424] Step 15:

[1425] The supporter receives the request, reviews the details, and creates a response.

[1426] Step 16:

[1427] The supporter's terminal transmits the created answer to the server.

[1428] Step 17:

[1429] The server provides the answers from the supporters to the user.

[1430] 6. Points allocation and management

[1431] Step 18:

[1432] If the supporter's answer is accepted, the server calculates points based on the point rules.

[1433] The server assigns points to the supporter's account.

[1434] Step 19:

[1435] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[1436] The supporter's terminal transmits this information to the server.

[1437] Step 20:

[1438] The server accepts the point exchange request and carries out the exchange procedure.

[1439] summary

[1440] This series of steps allows for efficient support between users and supporters, and optimal answers are provided using generative AI. Supporters can also receive appropriate rewards through a points system, improving the accuracy and effectiveness of the overall support system.

[1441] Example 1

[1442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1443] To address today's diverse support needs, systems are needed that allow users to easily input specific questions and quickly provide optimal answers. However, existing support systems have the following problems. First, they lack a process for clarifying the category of support a user needs, making it difficult to provide accurate support. Furthermore, there is no established method for collecting and utilizing feedback to improve the quality of answers generated by generative AI, resulting in low accuracy of answers. Finally, there is a lack of appropriate incentives for supporters, making it difficult for them to continue participating.

[1444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1445] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and for the supporter to exchange the points for goods or services; means for installing an application on a terminal and transmitting a name, email address, and password to the server; means for the user to select a category for which assistance is required and transmit that information to the server; means for the server to accumulate user information and feedback in a database; and means for the generation AI to train to improve its performance based on the accumulated data. This enables the system to accurately and efficiently provide the assistance required by the user, improve the accuracy of the generation AI's answers, and provide appropriate incentives to supporters, thereby achieving continuous and effective assistance.

[1446] "User" refers to an individual or group of people who require assistance and utilize the system to input a specific question.

[1447] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate optimal answers.

[1448] A "server" refers to a computer system that receives and stores information from users, interacts with the generating AI, and notifies supporters of requests.

[1449] "Supporter" refers to an individual or group who uses their specialized knowledge to provide answers to users' questions and earn points.

[1450] A "database" refers to a storage device for storing and managing user information, support activity data, feedback, etc.

[1451] "Points" refer to the rewards given to supporters for the answers they provide, and are units within the system that can be accumulated and exchanged for goods and services.

[1452] "Feedback" refers to ratings and comments that users make on answers provided.

[1453] "Installation" refers to the process of placing an application on a device and making it available for use.

[1454] A "question" refers to a sentence or group of sentences that a user inputs to the system with the specific matter for which they require assistance.

[1455] A "category" refers to a group for classifying the area of ​​support that a user needs.

[1456] A "prompt" refers to a question or instruction used to give appropriate instructions to the generative AI.

[1457] MODE FOR CARRYING OUT THE INVENTION

[1458] The present invention is a support system in which a user inputs a specific question, and a generation AI analyzes the question and provides the most appropriate answer. This system is composed of a user, a terminal, a server, and a generation AI. Detailed embodiments are described below.

[1459] User registration and initial settings

[1460] First, the user installs the application on their device. Once the installation is complete, the user launches the application and enters their name, email address, and password on the account creation screen. The device sends this information to the server. The server stores the received information in a database and generates a unique user ID. The generated user ID is sent back to the device. Next, the user selects the category for which they need assistance and sends this information to the server. The server stores the selected information in a database.

[1461] Accepting support requests

[1462] The user inputs a specific question for which they require assistance. For example, they can input a question such as, "What supermarkets in Tokyo are recommended?" The device then sends this question and the user ID to the server. The server stores the received question in a database and analyzes it using natural language processing (NLP) technology. Specifically, it uses libraries such as spaCy to extract appropriate categories and keywords from the question. The server then passes the analysis results to a generative AI, which generates a prompt. An example of a prompt is, "Generate an appropriate answer to the following question: 'What supermarkets in Tokyo are recommended?'"

[1463] Providing and Evaluating Answers

[1464] The generative AI analyzes the question based on the prompt text and generates an appropriate answer. The generated answer is sent back to the server and stored in a database. The server then sends the answer to the device and provides it to the user. The user checks the answer and enters feedback. For example, the answer provided might be, "A popular supermarket in Tokyo is XX supermarket." The user rates their satisfaction with the answer and sends feedback information to the server via their device. The server stores the feedback in a database and uses this data to learn and improve the performance of the generative AI.

[1465] Matching with supporters

[1466] If the question requires specialized knowledge, the server identifies an appropriate assistant from the list of assistants. The server notifies the assistant of the request for assistance, and the assistant receives the request. The assistant creates a specific answer and sends it to the server via the terminal. The server stores the answer in a database and provides it to the user.

[1467] Points allocation and management

[1468] The server awards points to supporters based on feedback from users. Points are calculated according to the quality of answers and the user's satisfaction. Supporters can check their points and request exchange for goods or services via their terminals. The server accepts the request and carries out the necessary procedures.

[1469] Data accumulation and analysis

[1470] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this data to learn and improve its performance. This enables it to provide more accurate answers in future support activities.

[1471] This system allows users to receive accurate and prompt assistance, and the helpers can earn points as an incentive. The generative AI can improve its performance through feedback, thereby increasing the overall quality of assistance.

[1472] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1473] Step 1:

[1474] The user installs the application on their device. After installation, they launch the application and enter their name, email address, and password on the account creation screen. The device sends this input data to the server. The server stores the received user information in a database, generates a unique user ID, and sends it back to the device. Specifically, the server creates a new record in the database, stores the name, email address, and password, and simultaneously generates an incrementing user ID.

[1475] Step 2:

[1476] The user selects the category for which they need assistance (e.g., general life, exams, employment). The device sends the selected category information along with the user ID to the server. The server saves this information in a database. Specifically, the server associates the selected category information with the user ID generated earlier in the database and stores it.

[1477] Step 3:

[1478] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device sends the question and the user ID to the server. The server then stores the received question in a database. Specifically, the server creates a new question record in the database and stores the question text and the user ID.

[1479] Step 4:

[1480] The server analyzes the received question using natural language processing (NLP) technology. For example, it uses spaCy to tokenize the text and extract keywords. The input is the question text, and the output is a list of extracted keywords. For example, the server analyzes the question "What supermarkets are recommended in Tokyo?" and extracts keywords such as "Tokyo," "recommended," and "supermarket."

[1481] Step 5:

[1482] The server generates a prompt based on the analysis results (extracted keywords and question text). The generated prompt is then sent to the generation AI (e.g., GPT-3). An example of a prompt is: "Please generate an appropriate answer to the following question: 'Please tell me the best supermarkets in Tokyo.'" The input is the analysis results (keyword list and question text), and the output is the generated prompt. Specifically, the server uses a prompt generation algorithm to construct a prompt and send it to the generation AI.

[1483] Step 6:

[1484] The generation AI generates an appropriate answer based on the prompt text. The generation AI receives the prompt text from the server and generates the answer text using an internal model. The input is the prompt text and the output is the answer text. Specifically, the generation AI goes through a generation process to generate an answer such as "A popular supermarket in Tokyo is XX supermarket" and sends this back to the server.

[1485] Step 7:

[1486] The server receives the generated answer and stores it in a database. It then sends the answer to the terminal and provides it to the user. The input is the answer text from the generation AI, and the output is the answer provided to the user. Specifically, the server creates a new answer record in the database, associates it with the user ID, and stores the answer text.

[1487] Step 8:

[1488] The user receives the response and checks its contents. They then enter feedback such as their level of satisfaction and areas for improvement. The device then sends the feedback information and the user ID to the server. The server then stores this feedback information in a database. The input is the user's feedback, and the output is the stored feedback data. Specifically, the server adds a feedback record to the database and associates it with the user ID.

[1489] Step 9:

[1490] The server uses the collected feedback as learning data to improve the performance of the generative AI. It analyzes the feedback information and adds it to the generative AI's training dataset. The input is user feedback, and the output is an updated training dataset. Specifically, the server analyzes the feedback data and provides new learning data to the generative AI.

[1491] Step 10:

[1492] If the question requires specialized knowledge, the server identifies an appropriate supporter from the supporter list and notifies the supporter of the support request. The input is the question content and analysis results, and the output is a request notification to the supporter. Specifically, the server searches the supporter list and sends a notification to the most suitable supporter.

[1493] Step 11:

[1494] The supporter receives the request and creates a specific answer. The answer is sent to the server via the terminal. The server stores the supporter's answer in a database and provides it to the user. The input is the answer created by the supporter, and the output is the answer provided to the user. In concrete terms, the server stores the received answer in a database and provides it to the user.

[1495] Step 12:

[1496] The server awards points to supporters based on user feedback. Points are calculated based on the quality of the answers and the user's satisfaction. The input is the feedback information, and the output is the awarded points. Specifically, the server calculates points using a feedback evaluation algorithm and adds them to the supporter's account.

[1497] Step 13:

[1498] Supporters use their terminals to check their points and request exchange for goods or services. The server accepts the request and carries out the necessary procedures. The input is the supporter's point exchange request, and the output is the provision of goods or services. Specifically, the server processes the exchange request and carries out the procedures to provide the supporter with goods or services.

[1499] (Application example 1)

[1500] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1501] Conventional food delivery systems require users to manually search for restaurants and menus, making it difficult to find the right option in the process. They also lack a mechanism for providing appropriate answers to user questions, resulting in a poor user experience. Furthermore, there is no mechanism for actively using user feedback to train the generative AI and improve the accuracy of answers, making it difficult to improve the quality of the service. There is a need for a system that solves these problems, allows users to easily enjoy optimal food delivery services, and improves the overall quality of the service.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1503] In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; and means for a user to input a specific question about food delivery and for the generation AI to suggest optimal stores and menus. This allows a user to receive optimal suggestions from the generation AI simply by inputting a specific question, and also enables the quality of service to be continuously improved through feedback.

[1504] "Generative AI" is an artificial intelligence technology that analyzes specific questions entered by users and generates optimal answers based on them.

[1505] A "support request" is request information sent to a supporter to ask for an answer to a user's question.

[1506] "Points" are units of virtual currency or credit awarded as a reward to supporters when they provide specific answers.

[1507] "Feedback" refers to information that a user returns through an application, such as their satisfaction with the answers provided, their ratings, or their opinions.

[1508] "Food delivery" is a system in which you order food and drinks and have them delivered to a designated location via a delivery service.

[1509] "User registration" is the process in which a user enters basic information such as name, email address, and password in order to use an application, and the server manages this information in a database.

[1510] A "supporter" is a person or system that has the ability to provide specialized knowledge and information in response to a user's questions.

[1511] The "server" is a central control device that receives input information and feedback from users and provides information to the generation AI and assistants.

[1512] A "database" is an information system that allows the server to centrally manage various data such as user information, questions, answers, feedback, and points.

[1513] "Means for suggesting optimal restaurants and menus" refers to a method that uses generative AI to recommend appropriate restaurants and dishes in response to specific food delivery-related questions from users.

[1514] The present invention is a system that improves the user experience in food delivery by using generative AI to provide optimal answers to specific questions entered by users. An embodiment of this system is described in detail below.

[1515] 1. User registration and initial settings

[1516] First, the user installs the application on their smartphone, smart glasses, or head-mounted display. When launching the application for the first time, the user enters basic information such as name, email address, and password on the account creation screen, which is then sent from the device to the server. The server then stores the user information in a database and generates a user ID. The user then selects a support category related to food delivery, and the device sends this information to the server. The server then stores the selected information in a database.

[1517] 2. Accepting support requests

[1518] The user inputs a specific question related to food delivery, such as "What do you recommend for dinner tonight?" The device then sends the question and the user ID to the server. The server analyzes the received question and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[1519] 3. Providing and Evaluating Answers

[1520] The generation AI understands the user's question, generates an appropriate answer, and sends it back to the server. The server provides the generated answer to the user. For example, this answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here." The user checks the provided answer and enters feedback on the content. The device sends the feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1521] 4. Matching with supporters

[1522] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[1523] 5. Points allocation and management

[1524] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[1525] 6. Data accumulation and analysis

[1526] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1527] Hardware and Software Used

[1528] Hardware:

[1529] Smartphones (general smart devices)

[1530] Smart glasses (general-purpose smart wearable device)

[1531] Head-mounted display (general VR / AR device)

[1532] software:

[1533] Generative AI models (natural language processing models such as GPT-3)

[1534] Server-side database (MySQL, PostgreSQL)

[1535] Specific examples

[1536] User Ask: "What are your dinner recommendations for tonight?"

[1537] Generative AI response: "The nearby restaurant, Restaurant A, has a good reputation. You can make a reservation here."

[1538] In this way, the system of the present invention provides quick and appropriate answers to user questions about food delivery, improving the user experience and utilizing feedback to continuously improve the accuracy of the service.

[1539] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1540] Step 1:

[1541] The user installs the application on their smartphone, smart glasses, or head-mounted display and launches it for the first time. The user enters basic information such as name, email address, and password on the account creation screen. The entered information is sent by the device to the server. The server stores the user information in a database and generates a user ID. The user selects a support category related to food delivery, and this information is also sent to the server and stored in the database.

[1542] Input: Name, Email Address, Password, Support Category

[1543] Output: User ID, user information stored in the database

[1544] Step 2:

[1545] Users input specific questions about food delivery into the app. For example, "What do you recommend for dinner tonight?" The input question and user ID are sent to the server by the device. The server analyzes the received question and extracts appropriate categories and keywords. The analysis results are then passed to the generation AI, which generates the optimal answer.

[1546] Input: Question, User ID

[1547] Output: Analysis results of the question, data passed to the generation AI

[1548] Step 3:

[1549] The generation AI understands the user's question and generates an appropriate answer. The generated answer is sent back to the server, which then provides the answer to the user. For example, the answer might be something like, "The nearby restaurant 'XX' has a good reputation. Please make a reservation here."

[1550] Input: User question, analysis result of the generating AI

[1551] Output: Generated answer, information provided to the user

[1552] Step 4:

[1553] The user checks the answers provided and enters feedback on the content into the app. The feedback information is sent by the device to the server, which stores it in a database. The collected feedback is used as learning data for the generative AI.

[1554] Input: Feedback information

[1555] Output: Feedback stored in a database, training data for generative AI

[1556] Step 5:

[1557] If the server determines that the question requires specialized knowledge, it identifies an appropriate supporter from the supporter list and notifies the user of the support request. The supporter receives the request, creates a specific answer, and sends it to the server. The server then provides the received answer to the user.

[1558] Input: Supporter list, support request

[1559] Output: Supporter's response, providing the response to the user

[1560] Step 6:

[1561] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can then check the points awarded to their account and request to exchange them for goods or services via their device. The server will then accept the request and carry out the goods exchange procedure.

[1562] Input: Supporter's answer, point rules

[1563] Output: Points awarded to supporters, product exchange procedure

[1564] Step 7:

[1565] The server stores data from all support activities (questions, answers, feedback, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1566] Input: Outreach data

[1567] Output: Improved performance of generative AI, more accurate answers

[1568] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1569] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[1570] 1. User registration and initial settings

[1571] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[1572] 2. Accepting support requests

[1573] The user inputs a specific question for which they require assistance. For example, "Please tell me which supermarkets are recommended in Tokyo." The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the AI, which generates the optimal answer.

[1574] 3. Emotion Recognition and Answer Generation

[1575] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[1576] 4. Providing and Evaluating Answers

[1577] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1578] 5. Matching with supporters

[1579] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1580] 6. Points allocation and management

[1581] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1582] 7. Data accumulation and analysis

[1583] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1584] The system based on this invention utilizes generative AI to streamline communication between users and supporters, improving the accuracy of support activities, and by combining it with an emotion engine, provides answers that take the user's emotions into consideration. In addition, a points system allows supporters to receive appropriate rewards, improving the accuracy and effectiveness of the entire support system.

[1585] The processing flow will be explained below.

[1586] Program processing steps

[1587] 1. User registration and initial settings

[1588] Step 1:

[1589] The user installs the application on the device and launches the application.

[1590] Step 2:

[1591] The user enters their name, email address, and password on the account creation screen.

[1592] The terminal transmits the input information to the server.

[1593] Step 3:

[1594] The server stores the user information in a database and generates a user ID.

[1595] Step 4:

[1596] The server returns the generated user ID to the terminal.

[1597] The terminal saves the user ID and moves to the next screen.

[1598] Step 5:

[1599] The user selects the category for which they need assistance (e.g., general life, exams, employment).

[1600] The terminal transmits the selected category information to the server.

[1601] Step 6:

[1602] The server stores the category information in a database.

[1603] 2. Accepting support requests

[1604] Step 7:

[1605] The user enters the specific question for which they need assistance.

[1606] The terminal sends the entered question and user ID to the server.

[1607] Step 8:

[1608] The server analyzes the content of the question using natural language processing technology.

[1609] Step 9:

[1610] The server extracts appropriate categories and keywords and passes them to the generation AI.

[1611] 3. Emotion Recognition and Answer Generation

[1612] Step 10:

[1613] The server sends the question content to the emotion engine.

[1614] The emotion engine recognizes the user's emotion based on the user's input.

[1615] Step 11:

[1616] The emotion engine returns the recognized emotion data to the server.

[1617] The server sends the emotion data to the generation AI.

[1618] Step 12:

[1619] The generative AI generates the optimal answer based on the question content and emotional data.

[1620] Step 13:

[1621] The generation AI sends the generated answer back to the server.

[1622] 4. Providing and Evaluating Answers

[1623] Step 14:

[1624] The server sends the answer received from the generation AI to the user.

[1625] Step 15:

[1626] The terminal receives the response and displays it to the user.

[1627] Step 16:

[1628] The user may enter their satisfaction with the answers provided and any additional feedback.

[1629] Step 17:

[1630] The terminal transmits the feedback information to the server.

[1631] Step 18:

[1632] The server stores the received feedback in a database and uses it as training data for the generative AI.

[1633] 5. Matching with supporters

[1634] Step 19:

[1635] If the server determines that expertise is needed for a particular question, it identifies an appropriate helper from the helper list.

[1636] Step 20:

[1637] The server notifies the supporter of the support request.

[1638] Step 21:

[1639] The supporter receives the request, reviews the details, and creates a response.

[1640] Step 22:

[1641] The supporter's terminal transmits the created answer to the server.

[1642] Step 23:

[1643] The server provides the answers from the supporters to the user.

[1644] 6. Points allocation and management

[1645] Step 24:

[1646] If the supporter's answer is accepted, the server calculates points based on the point rules.

[1647] Step 25:

[1648] The server assigns points to the supporter's account.

[1649] Step 26:

[1650] Supporters can check the points they have been awarded and submit a request to exchange them for goods or services.

[1651] Step 27:

[1652] The supporter's terminal transmits this information to the server.

[1653] Step 28:

[1654] The server accepts the point exchange request and carries out the exchange procedure.

[1655] 7. Data accumulation and analysis

[1656] Step 29:

[1657] The server stores all support activity data (questions, answers, feedback, emotional data, etc.) in a database.

[1658] Step 30:

[1659] The generative AI uses this accumulated data to learn and improve its own performance.

[1660] summary

[1661] This series of steps allows for efficient support between users and supporters, and the generative AI provides optimal answers. Furthermore, by combining it with an emotion engine, answers can be provided that take the user's emotions into consideration. Furthermore, supporters can receive appropriate rewards through a point system, improving the accuracy and effectiveness of the overall support system.

[1662] Example 2

[1663] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1664] Conventional support systems have had the problem that the answers users receive to their questions are not always appropriate to the user's feelings or situation. Furthermore, the answers provided do not fully utilize the supporter's expertise, limiting the quality and effectiveness of the support. Furthermore, supporters are not properly compensated, which can lead to a decline in motivation for support activities.

[1665] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1666] In this invention, the server includes: a means for inputting a specific question for which a user requires assistance; a means for analyzing the question using a generation AI and generating an optimal answer; a means for providing the generated answer to the user and collecting feedback from the user; a means for training the generation AI based on the collected feedback; a means for recognizing the user's emotions based on the content of the question; a means for transmitting emotion data to the generation AI and generating an answer that takes emotions into consideration; a means for identifying a supporter and notifying the supporter of a support request; a means for awarding points based on the answer provided by the supporter; and a means for managing the points and allowing the supporter to exchange the points for goods or services. This enables the provision of answers that take the user's emotions into consideration and high-quality support by matching the user with supporters with specialized knowledge. Furthermore, the point system can provide appropriate rewards to supporters, thereby increasing their motivation for support activities.

[1667] "User" refers to a person who uses the system to seek assistance.

[1668] A "specific question requiring assistance" refers to a question that includes a specific problem or uncertainty that the user wants resolved.

[1669] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze users' questions and generate the most appropriate answers.

[1670] An "emotion engine" refers to a system that recognizes a user's emotions based on the questions they enter and provides this as data to the generative AI.

[1671] "Feedback" refers to ratings and comments that users make on answers provided.

[1672] A "supporter" refers to a person who has specific knowledge and skills and whose role is to provide expert answers to users' questions.

[1673] "Points" refer to a form of reward given to a supporter when the supporter provides appropriate support to a user.

[1674] "Generated answer" refers to an answer generated by the generation AI based on the user's question.

[1675] "Notifying" refers to the act of informing relevant parties of specific information.

[1676] "Exchange" refers to the act of converting awarded points into concrete value such as goods or services.

[1677] The present invention provides an effective support system for both the supporter and the support recipient, in which the user inputs a specific question for which support is needed and the generation AI provides the optimal answer. In particular, by combining it with an emotion engine, it becomes possible to recognize the user's emotions and provide an answer that takes these into consideration. An embodiment of this system is described in detail below.

[1678] User registration and initial settings

[1679] First, the user installs the application on their device and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which the device sends to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they need (e.g., general life, exams, employment), and the device sends this information to the server. The server saves the user's selection in a database.

[1680] Accepting support requests

[1681] The user inputs a specific question for which they require assistance. For example, "What supermarkets in Tokyo are recommended?" The device then sends the question and the user ID to the server. The server then analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to a generative AI, which generates the optimal answer. The generative AI model used here is an advanced natural language processing model such as GPT-4.

[1682] Emotion Recognition and Answer Generation

[1683] The emotion engine recognizes emotions based on the user's input. For example, if the user inputs something that expresses urgency or difficulty, the emotion engine will recognize that the user is anxious or confused. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more sympathetic to the user. For example, a response such as "Don't rush, X supermarket is convenient and has a good reputation."

[1684] Providing and Evaluating Answers

[1685] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1686] Matching with supporters

[1687] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1688] Points allocation and management

[1689] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1690] Data accumulation and analysis

[1691] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1692] Specific examples

[1693] User: "What supermarkets in Tokyo do you recommend?"

[1694] Generative AI response: "Don't worry, Ameyoko Market is popular."

[1695] Prompt Sentence Examples

[1696] A user has asked, "What supermarkets are recommended in Tokyo?" You can sense the user's impatience. Please generate an answer that introduces appropriate supermarkets in a way that is considerate of the user.

[1697] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1698] Step 1: User registration and initial setup

[1699] Input: The user enters their name, email address, and password.

[1700] How it works: After installing the app, the user launches it for the first time and is taken to the account creation screen. The user enters their name, email address, and password.

[1701] Data processing: The terminal sends this information to the server, which stores the user information in a database and generates a user ID.

[1702] Output: The server returns the generated user ID to the terminal, and the user ID is displayed on the terminal.

[1703] Step 2: Select a support category

[1704] Input: The user selects the category for which they need assistance (e.g., general life, exams, employment).

[1705] How it works: The user selects an assistance category in the settings screen within the app.

[1706] Data processing: The terminal sends the selected information to the server, which stores it in a database.

[1707] Output: The server returns a save completion message to the terminal, and the support category selected by the user is displayed on the terminal.

[1708] Step 3: Accepting a request for assistance

[1709] Input: The user enters a specific question. Example: "What supermarkets in Tokyo do you recommend?"

[1710] Operation: The user enters a question on the question screen and presses the send button.

[1711] Data processing: The device sends the question and user ID to the server. The server then analyzes the received question using natural language processing technology and extracts appropriate categories and keywords.

[1712] Output: The server passes the analysis results to the generation AI to obtain the optimal answer. The generated answer is saved on the server.

[1713] Step 4: Emotion recognition and answer generation

[1714] Input: The emotion engine receives the question. At the same time, the analysis results are sent to the generation AI.

[1715] How it works: The emotion engine analyzes the question and recognizes the user's emotions. For example, it analyzes the question "I need help urgently" and determines that the user is in a hurry.

[1716] Data processing: Emotional data is sent to the generation AI, which then generates an answer based on the emotional data and analysis results.

[1717] Output: The generative AI generates an answer that takes emotions into account, and this answer is sent back to the server.

[1718] Step 5: Provide answers and gather feedback

[1719] Input: Generated Answer

[1720] Operation: The server generates a response and sends it to the user's device. The user receives the response and checks its contents.

[1721] Data processing: User feedback is input and the device sends it to the server.

[1722] Output: The feedback stored on the server is recorded in a database and used as training data for the generative AI.

[1723] Step 6: Matching with donors

[1724] Input: Question content and user category information

[1725] How it works: The server reviews the question and determines if expertise is required. It then identifies an appropriate helper from the helper list and notifies them of the assistance request.

[1726] Data processing: After the supporter receives the request, they create a specific response and send it to the server.

[1727] Output: The server receives the supporter's answer and provides it to the user.

[1728] Step 7: Earn and manage your points

[1729] Input: Accepted supporter's answer, point rules

[1730] Action: The server awards points to the supporter.

[1731] Data processing: Points will be added to the supporter's account.

[1732] Output: Supporters can check their points and request to exchange them for goods or services through their terminal. The server accepts the exchange request and carries out the goods exchange procedure.

[1733] Step 8: Data collection and analysis

[1734] Input: All support activity data, including questions, answers, feedback, and emotional data

[1735] How it works: All data is stored on the server.

[1736] Data processing: The generative AI learns based on this accumulated data and aims to improve its performance.

[1737] Output: A more accurate answer is generated.

[1738] (Application example 2)

[1739] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1740] Conventional support systems have been inadequate in responding to users' emotional states and specific needs, leaving the improvement of user experience a challenge. Furthermore, in the content distribution service field, it has been difficult to accurately recommend content that users want to watch. This has made it difficult to provide the support users require quickly and appropriately.

[1741] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting a specific question for which a user requires assistance; means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answer to the user and collecting feedback from the user; means for training the generation AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on the answer provided by the supporter; means for managing the points and allowing the supporter to exchange the points for goods or services; means for recognizing the user's emotions using an emotion engine and generating an optimal answer taking those emotions into consideration; and means for recommending content that the user wants to view in a content distribution service. This makes it possible to provide appropriate assistance that takes the user's emotions and needs into consideration and accurately recommend content that the user wants to view.

[1742] A "user" is an individual who uses the support system and inputs a specific question for which an answer is sought.

[1743] A "supporter" is an individual or organization that has specialized knowledge and experience and provides answers to users' questions.

[1744] "Generative AI" is an artificial intelligence technology that analyzes input questions and generates optimal answers.

[1745] An "emotion engine" is a technology that recognizes emotions from user input and generates appropriate answers based on that.

[1746] "Feedback" refers to ratings and additional comments from users who receive answers.

[1747] "Points" are units within the system that are awarded as part of the reward to supporters and can be exchanged for goods and services.

[1748] A "content distribution service" is an online service that provides digital content such as movies, music, and books.

[1749] A "viewing history" is a record of content that a user has viewed in the past.

[1750] "Recommendation means" is a technology that selects and provides optimal content based on the user's questions, emotional state, viewing history, etc.

[1751] The "server" is a central system that manages data, analyzes questions, runs generative AI, operates the emotion engine, collects feedback, and manages points.

[1752] This invention is a system that allows users to input specific questions for which they need assistance and provides optimal answers by combining generative AI and an emotion engine. In particular, it includes a function for recommending content based on the user's viewing history and emotions in content distribution services.

[1753] 1. User registration and initial settings

[1754] First, the user installs the application on their smartphone and launches it for the first time. Next, they enter basic information such as their name, email address, and password on the account creation screen, which is then sent to the server. The server saves the user information in a database and generates a user ID. The user selects the category of support they require (e.g., movie recommendations, music recommendations), and the device sends this information to the server. The server saves the user's selection in a database.

[1755] 2. Accepting support requests

[1756] The user inputs a specific question for which they require assistance. For example, "I'm looking for a relaxing movie." The device sends the question and the user ID to the server. The server analyzes the question using natural language processing technology and extracts appropriate categories and keywords. The server then passes the analysis results to the generation AI, which generates the optimal answer.

[1757] 3. Emotion Recognition and Answer Generation

[1758] The emotion engine recognizes emotions based on the user's input. For example, if a user sends a request such as "I feel like watching a funny movie lately," the emotion engine recognizes that the user is looking for relaxation and fun. The server sends the emotion data recognized by the emotion engine to the generation AI. The generation AI takes this emotion data into consideration and generates a response that is more in line with the user's needs. For example, a response such as "What do you think of the latest popular comedy movies?"

[1759] 4. Providing and Evaluating Answers

[1760] The generation AI generates an answer that takes the user's feelings into consideration and sends it back to the server. The server provides the generated answer to the user and requests feedback from the user. The user receives the answer and checks its content. The user then enters their satisfaction with the answer and any additional feedback. The device then sends this feedback information to the server. The server stores the feedback in a database and uses it as learning data for the generation AI.

[1761] 5. Matching with supporters

[1762] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server then notifies the supporter of the support request. The supporter receives the request, creates a specific answer, and sends it to the server via the terminal. The server then provides the received answer from the supporter to the user.

[1763] 6. Points allocation and management

[1764] Once the answer is accepted, the server will award points to the supporter based on the point rules. The supporter can check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept the request and carry out the goods exchange procedure.

[1765] 7. Data accumulation and analysis

[1766] The server stores data from all support activities (questions, answers, feedback, emotional data, etc.) in a database. The generative AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity.

[1767] Specific examples

[1768] For example, if a user asks, "I'm tired these days, so I'm looking for a relaxing movie," the emotion engine recognizes the user's desire to relax. Based on that, the generative AI generates the answer, "How about a relaxing movie that's popular these days to help you relax?"

[1769] Prompt Sentence Examples

[1770] "I'm looking for a relaxing movie."

[1771] "I feel like watching a funny movie lately."

[1772] As described above, the embodiments of the invention are configured to understand the feelings and needs of the user and provide appropriate content and support.

[1773] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1774] Step 1:

[1775] A user installs the application on their smartphone and launches it for the first time. The user enters their name, email address, and password on the account creation screen, and the device sends this to the server. The server receives the user information as input data, generates a user ID, and stores this information in a database. The server generates a user ID as output data and sends it to the device.

[1776] Step 2:

[1777] The user enters a specific question for which they require assistance into the application. For example, they might enter a question like, "I'm looking for a relaxing movie." The device then sends the user ID and the question to the server. The server receives the user's question and ID as input data, analyzes the question using natural language processing technology, and extracts appropriate categories and keywords. The analyzed categories and keywords are then generated as output data and passed to the next processing step.

[1778] Step 3:

[1779] The server passes the analysis results to the generation AI, which generates the optimal answer. The generation AI receives the analyzed categories and keywords as input data and generates the optimal answer based on this. As a specific data calculation, it generates related answer candidates and selects the optimal one from among them. The generated answer is obtained as output data.

[1780] Step 4:

[1781] The emotion engine recognizes emotions based on the user's input. Specifically, it analyzes the text content of the user's input and uses an algorithm to determine the emotional state. It receives the user's question text as input data and generates the recognized emotional state as output data. The server sends the emotion data recognized by the emotion engine to the generation AI.

[1782] Step 5:

[1783] Generative AI takes emotional data into consideration to generate answers that are more in line with the user's needs. For example, it generates an answer such as, "How about watching a relaxing movie that's popular these days to soothe your fatigue?" It receives the emotion recognition results and question analysis results as input data, and adjusts the answer based on the emotion as data processing. It generates the final answer as output data.

[1784] Step 6:

[1785] The server provides the generated answer to the user. The server sends the answer received from the generation AI to the user's device, where it is received by the user. The server receives the generated answer as input data and sends it to the user's device as output data.

[1786] Step 7:

[1787] The user inputs their satisfaction with the answers provided and any additional feedback. The device then sends the feedback information to the server. The server receives the feedback as input data and stores it in a database. The feedback information is used as data for training the generative AI.

[1788] Step 8:

[1789] If the server determines that specialized knowledge is required for the question, it identifies an appropriate supporter from the supporter list. The server notifies the supporter of the support request and provides input data for the supporter to create a specific answer. The server then provides the supporter's answer to the user, delivering appropriate support to the user as output data.

[1790] Step 9:

[1791] Once the response is accepted, the server will award points to the supporter based on the point rules. The supporter will check the points awarded to their account and request to exchange them for goods or services via their terminal. The server will accept this request and execute the goods exchange procedure. It will receive the supporter's point award information and exchange request as input data, and output confirmation of the goods exchange as output data.

[1792] Step 10:

[1793] The server stores all support activity data (question content, answers, feedback, emotional data, etc.) in a database. The generating AI uses this stored data to learn and improve its own performance. This enables it to provide more accurate answers in the next support activity. The server receives the stored support activity data as input data and generates learning data as output data that contributes to improving the performance of the generating AI.

[1794] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1795] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1796] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1797] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1798] FIG. 9 is a diagr...

Claims

1. a means for the user to input the specific question for which they require assistance; A means for analyzing the question using a generation AI and generating an optimal answer; means for providing the generated answers to the user and collecting feedback from the user; A means for training the generative AI based on the collected feedback; means for identifying a supporter and notifying the supporter of a support request; means for awarding points based on answers provided by supporters; A means for managing said points and for supporters to exchange said points for goods or services; A system including:

2. 10. The system of claim 1, further comprising means for a user to select an area in which assistance is needed.

3. The system of claim 1 further comprising means for accepting an answer provided by a supporter and providing the answer to the user.

Images